International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
PORTFOLIO SELECTION BY THE MEANS OF CUCKOO OPTIMIZATION ALGORITHMijcsa
Portfolio selection is one of the most important and vital decisions that a real or legal person, who invests in stock market should make. The main purpose of this paper is the determination of the optimal portfolio with regard to stock returns of companies, which are active in Tehran’s stock market. For achieving this purpose, annual statistics of companies’ stocks since Farvardin 1387 until Esfand 1392 have been used. For analyzing statistics, information of companies’ stocks, the Cuckoo Optimization Algorithm (COA) and Knapsack Problem have been used with the aim of increasing the total return, in order to form a financial portfolio. At last, results merits of choosing the optimal portfolio using the COA rather than Genetic
Algorithm are given.
POSSIBILISTIC SHARPE RATIO BASED NOVICE PORTFOLIO SELECTION MODELScscpconf
This paper uses the concept of possibilistic risk aversion to propose a new approach for portfolio selection in fuzzy environment. Using possibility theory, the possibilistic mean,
variance, standard deviation and risk premium of a fuzzy number are established. Possibilistic Sharpe ratio is defined as the ratio of possibilistic risk premium and possibilistic standard deviation of a portfolio. The Sharpe ratio is a measure of the performance of the portfolio compared to the risk taken. The higher the Sharpe ratio, the better the performance of the portfolio is and the greater the profits of taking risk. New models of fuzzy portfolio selection
considering the possibilistic Sharpe ratio, return and skewness of the portfolio are considered. The feasibility and effectiveness of the proposed method is illustrated by numerical example extracted from Bombay Stock Exchange (BSE), India and is solved by multiple objective genetic
algorithm (MOGA).
Improving Returns from the Markowitz Model using GA- AnEmpirical Validation o...idescitation
Portfolio optimization is the task of allocating the investors capital among
different assets in such a way that the returns are maximized while at the same time, the
risk is minimized. The traditional model followed for portfolio optimization is the
Markowitz model [1], [2],[3]. Markowitz model, considering the ideal case of linear
constraints, can be solved using quadratic programming, however, in real-life scenario, the
presence of nonlinear constraints such as limits on the number of assets in the portfolio, the
constraints on budgetary allocation to each asset class, transaction costs and limits to the
maximum weightage that can be assigned to each asset in the portfolio etc., this problem
becomes increasingly computationally difficult to solve, ie NP-hard. Hence, soft computing
based approaches seem best suited for solving such a problem. An attempt has been made in
this study to use soft computing technique (specifically, Genetic Algorithms), to overcome
this issue. In this study, Genetic Algorithm (GA) has been used to optimize the parameters
of the Markowitz model such that overall portfolio returns are maximized with the standard
deviation of the returns being minimized at the same time. The proposed system is validated
by testing its ability to generate optimal stock portfolios with high returns and low standard
deviations with the assets drawn from the stocks traded on the Bombay Stock Exchange
(BSE). Results show that the proposed system is able to generate much better portfolios
when compared to the traditional Markowitz model.
A Fuzzy Algorithm for Mining High Utility Rare Itemsets – FHURIidescitation
Classical frequent itemset mining identifies frequent itemsets in transaction
databases using only frequency of item occurrences, without considering utility of items. In
many real world situations, utility of itemsets are based upon user’s perspective such as
cost, profit or revenue and are of significant importance. Utility mining considers using
utility factors in data mining tasks. Utility-based descriptive data mining aims at
discovering itemsets with high total utility is termed High Utility Itemset mining. High
Utility itemsets may contain frequent as well as rare itemsets. Classical utility mining only
considers items and their utilities as discrete values. In real world applications, such utilities
can be described by fuzzy sets. Thus itemset utility mining with fuzzy modeling allows item
utility values to be fuzzy and dynamic over time. In this paper, an algorithm, FHURI (Fuzzy
High Utility Rare Itemset Mining) is presented to efficiently and effectively mine very-high
(and high) utility rare itemsets from databases, by fuzzification of utility values. FHURI can
effectively extract fuzzy high utility rare itemsets by integrating fuzzy logic with high utility
rare itemset mining. FHURI algorithm may have practical meaning to real-world
marketing strategies. The results are shown using synthetic datasets.
A NOVEL APPROACH TO MINE FREQUENT PATTERNS FROM LARGE VOLUME OF DATASET USING...IAEME Publication
In this paper, MDL based reduction in frequent pattern is presented. The ideal outcome of any pattern mining process is to explore the data in new insights. And also, we need to eliminate the non-interesting patterns that describe noise. The major problem in frequent pattern mining is to identify the interesting patterns. Instead of performing association rule mining on all the frequent item sets, it is feasible to select a sub set of frequent item sets and perform the mining task. Selecting a small set of frequent item sets from large amount of interesting ones is a difficult task. In our approach, MDL based algorithm is used for reducing the number of frequent item sets to be used for association rule mining is presented.
To demonstrate our approaches we will use Sudoku puzzles, which are an excellent test bed for
evolutionary algorithms. The puzzles are accessible enough for people to enjoy. However the more complex
puzzles require thousands of iterations before an evolutionary algorithm finds a solution. If we were
attempting to compare evolutionary algorithms we could count their iterations to solution as an indicator
of relative efficiency. Evolutionary algorithms however include a process of random mutation for solution
candidates. We will show that by improving the random mutation behaviours we were able to solve
problems with minimal evolutionary optimisation. Experiments demonstrated the random mutation was at
times more effective at solving the harder problems than the evolutionary algorithms. This implies that the
quality of random mutation may have a significant impact on the performance of evolutionary algorithms
with Sudoku puzzles. Additionally this random mutation may hold promise for reuse in hybrid evolutionary
algorithm behaviours.
Using genetic algorithms and simulation as decision support in marketing stra...infopapers
F.Stoica, L.F.Cacovean, Using genetic algorithms and simulation as decision support in marketing strategies and long-term production planning, Proceedings of the 9th WSEAS International Conference on SIMULATION, MODELLING AND OPTIMIZATION (SMO ‘09), Budapest Tech, Hungary, September 3-5, ISSN: 1790-2769 ISBN:978-960-474-113-7, pp. 435-439, 2009
PORTFOLIO SELECTION BY THE MEANS OF CUCKOO OPTIMIZATION ALGORITHMijcsa
Portfolio selection is one of the most important and vital decisions that a real or legal person, who invests in stock market should make. The main purpose of this paper is the determination of the optimal portfolio with regard to stock returns of companies, which are active in Tehran’s stock market. For achieving this purpose, annual statistics of companies’ stocks since Farvardin 1387 until Esfand 1392 have been used. For analyzing statistics, information of companies’ stocks, the Cuckoo Optimization Algorithm (COA) and Knapsack Problem have been used with the aim of increasing the total return, in order to form a financial portfolio. At last, results merits of choosing the optimal portfolio using the COA rather than Genetic
Algorithm are given.
POSSIBILISTIC SHARPE RATIO BASED NOVICE PORTFOLIO SELECTION MODELScscpconf
This paper uses the concept of possibilistic risk aversion to propose a new approach for portfolio selection in fuzzy environment. Using possibility theory, the possibilistic mean,
variance, standard deviation and risk premium of a fuzzy number are established. Possibilistic Sharpe ratio is defined as the ratio of possibilistic risk premium and possibilistic standard deviation of a portfolio. The Sharpe ratio is a measure of the performance of the portfolio compared to the risk taken. The higher the Sharpe ratio, the better the performance of the portfolio is and the greater the profits of taking risk. New models of fuzzy portfolio selection
considering the possibilistic Sharpe ratio, return and skewness of the portfolio are considered. The feasibility and effectiveness of the proposed method is illustrated by numerical example extracted from Bombay Stock Exchange (BSE), India and is solved by multiple objective genetic
algorithm (MOGA).
Improving Returns from the Markowitz Model using GA- AnEmpirical Validation o...idescitation
Portfolio optimization is the task of allocating the investors capital among
different assets in such a way that the returns are maximized while at the same time, the
risk is minimized. The traditional model followed for portfolio optimization is the
Markowitz model [1], [2],[3]. Markowitz model, considering the ideal case of linear
constraints, can be solved using quadratic programming, however, in real-life scenario, the
presence of nonlinear constraints such as limits on the number of assets in the portfolio, the
constraints on budgetary allocation to each asset class, transaction costs and limits to the
maximum weightage that can be assigned to each asset in the portfolio etc., this problem
becomes increasingly computationally difficult to solve, ie NP-hard. Hence, soft computing
based approaches seem best suited for solving such a problem. An attempt has been made in
this study to use soft computing technique (specifically, Genetic Algorithms), to overcome
this issue. In this study, Genetic Algorithm (GA) has been used to optimize the parameters
of the Markowitz model such that overall portfolio returns are maximized with the standard
deviation of the returns being minimized at the same time. The proposed system is validated
by testing its ability to generate optimal stock portfolios with high returns and low standard
deviations with the assets drawn from the stocks traded on the Bombay Stock Exchange
(BSE). Results show that the proposed system is able to generate much better portfolios
when compared to the traditional Markowitz model.
A Fuzzy Algorithm for Mining High Utility Rare Itemsets – FHURIidescitation
Classical frequent itemset mining identifies frequent itemsets in transaction
databases using only frequency of item occurrences, without considering utility of items. In
many real world situations, utility of itemsets are based upon user’s perspective such as
cost, profit or revenue and are of significant importance. Utility mining considers using
utility factors in data mining tasks. Utility-based descriptive data mining aims at
discovering itemsets with high total utility is termed High Utility Itemset mining. High
Utility itemsets may contain frequent as well as rare itemsets. Classical utility mining only
considers items and their utilities as discrete values. In real world applications, such utilities
can be described by fuzzy sets. Thus itemset utility mining with fuzzy modeling allows item
utility values to be fuzzy and dynamic over time. In this paper, an algorithm, FHURI (Fuzzy
High Utility Rare Itemset Mining) is presented to efficiently and effectively mine very-high
(and high) utility rare itemsets from databases, by fuzzification of utility values. FHURI can
effectively extract fuzzy high utility rare itemsets by integrating fuzzy logic with high utility
rare itemset mining. FHURI algorithm may have practical meaning to real-world
marketing strategies. The results are shown using synthetic datasets.
A NOVEL APPROACH TO MINE FREQUENT PATTERNS FROM LARGE VOLUME OF DATASET USING...IAEME Publication
In this paper, MDL based reduction in frequent pattern is presented. The ideal outcome of any pattern mining process is to explore the data in new insights. And also, we need to eliminate the non-interesting patterns that describe noise. The major problem in frequent pattern mining is to identify the interesting patterns. Instead of performing association rule mining on all the frequent item sets, it is feasible to select a sub set of frequent item sets and perform the mining task. Selecting a small set of frequent item sets from large amount of interesting ones is a difficult task. In our approach, MDL based algorithm is used for reducing the number of frequent item sets to be used for association rule mining is presented.
To demonstrate our approaches we will use Sudoku puzzles, which are an excellent test bed for
evolutionary algorithms. The puzzles are accessible enough for people to enjoy. However the more complex
puzzles require thousands of iterations before an evolutionary algorithm finds a solution. If we were
attempting to compare evolutionary algorithms we could count their iterations to solution as an indicator
of relative efficiency. Evolutionary algorithms however include a process of random mutation for solution
candidates. We will show that by improving the random mutation behaviours we were able to solve
problems with minimal evolutionary optimisation. Experiments demonstrated the random mutation was at
times more effective at solving the harder problems than the evolutionary algorithms. This implies that the
quality of random mutation may have a significant impact on the performance of evolutionary algorithms
with Sudoku puzzles. Additionally this random mutation may hold promise for reuse in hybrid evolutionary
algorithm behaviours.
Using genetic algorithms and simulation as decision support in marketing stra...infopapers
F.Stoica, L.F.Cacovean, Using genetic algorithms and simulation as decision support in marketing strategies and long-term production planning, Proceedings of the 9th WSEAS International Conference on SIMULATION, MODELLING AND OPTIMIZATION (SMO ‘09), Budapest Tech, Hungary, September 3-5, ISSN: 1790-2769 ISBN:978-960-474-113-7, pp. 435-439, 2009
Dissertation template bcu_format_belinda -sampleAssignment Help
Dear student, Warm Greetings of the Day!!! We are a qualified team of consultants and writers who provide support and assistance to students with their Assignments, Essays and Dissertation. If you are having difficulties writing your work, finding it stressful in completing your work or have no time to complete your work yourself, then look no further. We have assisted many students with their projects. Our aim is to help and support students when they need it the most. We oversee your work to be completed from start to end. We specialize in a number of subject areas including, Business, Accounting, Economic, Nursing, Health and Social Care, Criminology, Sociology, English, Law, IT, History, Religious Studies, Social Sciences, Biology, Physic, Chemistry, Psychology and many more. Our consultants are highly qualified in providing the highest quality of work to students. Each work will be unique and not copied like others. You can count on us as we are committed to assist you in producing work of the highest quality. Waiting for your quick response and want to start healthy long term relationship with you. Regards http://www.cheapassignmenthelp.com/ http://www.cheapassignmenthelp.co.uk/
Result analysis of mining fast frequent itemset using compacted dataijistjournal
Data mining and knowledge discovery of database is magnetizing wide array of non-trivial research arena,
making easy to industrial decision support systems and continues to expand even beyond imagination in
one such promising field like Artificial Intelligence and facing the real world challenges. Association rules
forms an important paradigm in the field of data mining for various databases like transactional database,
time-series database, spatial, object-oriented databases etc. The burgeoning amount of data in multiple
heterogeneous sources coalesces with the impediment in building and preserving central vital repositories
compels the need for effectual distributive mining techniques.
The majority of the previous studies rely on an Apriori-like candidate set generation-and-test approach.
For these applications, these forms of aged techniques are found to be quite expensive, sluggish and highly
subjective in case there exists long length patterns.
Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...Sri Ambati
Abstract:
Explainability in the age of the EU GDPR is becoming an increasingly pertinent consideration for Machine Learning. At QuantumBlack, we address the traditional Accuracy vs. Interpretability trade-off, by leveraging modern XAI techniques such as LIME and SHAP, to enable individualised explanations without necessary limiting the utility and performance of the otherwise ‘black-box’ models. The talk focuses on Shapley additive explanations (Lundberg et al. 2017) that integrate Shapley values from the Game Theory for consistent and locally accurate explanations; provides illustrative examples and touches upon the wider XAI theory.
Bio:
Dr Torgyn Shaikhina is a Data Scientist at QuantumBlack, STEM Ambassador, and the founder of the Next Generation Programmers outreach initiative. Her background is in decision support systems for Healthcare and Biomedical Engineering with a focus on Machine Learning with limited information.
Ties Adjusted Nonparametric Statististical Method For The Analysis Of Ordered...inventionjournals
This paper proposes a nonparametric statistical method for the analysis of repeated measures
that adjusts for the possibility of tied observation in the data which may be observations in time, space or
condition. The proposed method develops a test statistic for testing whether subjects are progressively
improving (or getting worse) in their experience over time, space or condition. The method is illustrated with
some data and shown to be at least as powerful as the Friedman’s two way analysis of variance test by ranks
even in the absence of any in-built ordering in the variable under study
Identify factors contributing increasing the use of POS in agricultural bank...inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Destructions and Progress: Removing Social Misconceptions on Environmental Is...inventionjournals
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Decision Management: The Dowsing Rod of ChangeMartijn Zoet
Business rules change at an increasing pace which is caused by rapidly changing customer requirements, legislation and policy. Simultaneously, a shift arises that demands an enhanced transparency with regard to the business operations of organizations. This shift forces organizations to demonstrate which business rules are applied in which situations. In order to be able to keep up with the high frequency of change and to comply with the transparency demands, the management of decisions and underlying business rules becomes increasingly important.
In this article, a classification scheme is presented which can be used to categorize the possible modifications with regard to information systems that execute decisions. Concerning these modifications, a distinction is made between the effort of the modification for the internal organization and the effort for a client.
Design of a Filtration System for a Small Scale Water Treatment Plant for a R...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
SWAREET is the last Project of Bakeri City Phase-1. The project is located on Opp. Smarana Apartments, Bakeri City, Close to prahladnagar, Vejalpur. The project is part of well developed Bakeri City which comprises of more than 5,000 residential units.
Just 5 minutes drive from Prahladnagar. Developing destinations like S.G. Road and Makarba are in close proximity of Bakeri City.
The proposed project consists of 530 well - designed 1, 1.5, 2 BHK Homes.
The project shall have well developed common amenities along with allotted parking space/s for two and four wheelers.The proposed residential units are spread over basement / parking and 5 floors.
Dissertation template bcu_format_belinda -sampleAssignment Help
Dear student, Warm Greetings of the Day!!! We are a qualified team of consultants and writers who provide support and assistance to students with their Assignments, Essays and Dissertation. If you are having difficulties writing your work, finding it stressful in completing your work or have no time to complete your work yourself, then look no further. We have assisted many students with their projects. Our aim is to help and support students when they need it the most. We oversee your work to be completed from start to end. We specialize in a number of subject areas including, Business, Accounting, Economic, Nursing, Health and Social Care, Criminology, Sociology, English, Law, IT, History, Religious Studies, Social Sciences, Biology, Physic, Chemistry, Psychology and many more. Our consultants are highly qualified in providing the highest quality of work to students. Each work will be unique and not copied like others. You can count on us as we are committed to assist you in producing work of the highest quality. Waiting for your quick response and want to start healthy long term relationship with you. Regards http://www.cheapassignmenthelp.com/ http://www.cheapassignmenthelp.co.uk/
Result analysis of mining fast frequent itemset using compacted dataijistjournal
Data mining and knowledge discovery of database is magnetizing wide array of non-trivial research arena,
making easy to industrial decision support systems and continues to expand even beyond imagination in
one such promising field like Artificial Intelligence and facing the real world challenges. Association rules
forms an important paradigm in the field of data mining for various databases like transactional database,
time-series database, spatial, object-oriented databases etc. The burgeoning amount of data in multiple
heterogeneous sources coalesces with the impediment in building and preserving central vital repositories
compels the need for effectual distributive mining techniques.
The majority of the previous studies rely on an Apriori-like candidate set generation-and-test approach.
For these applications, these forms of aged techniques are found to be quite expensive, sluggish and highly
subjective in case there exists long length patterns.
Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...Sri Ambati
Abstract:
Explainability in the age of the EU GDPR is becoming an increasingly pertinent consideration for Machine Learning. At QuantumBlack, we address the traditional Accuracy vs. Interpretability trade-off, by leveraging modern XAI techniques such as LIME and SHAP, to enable individualised explanations without necessary limiting the utility and performance of the otherwise ‘black-box’ models. The talk focuses on Shapley additive explanations (Lundberg et al. 2017) that integrate Shapley values from the Game Theory for consistent and locally accurate explanations; provides illustrative examples and touches upon the wider XAI theory.
Bio:
Dr Torgyn Shaikhina is a Data Scientist at QuantumBlack, STEM Ambassador, and the founder of the Next Generation Programmers outreach initiative. Her background is in decision support systems for Healthcare and Biomedical Engineering with a focus on Machine Learning with limited information.
Ties Adjusted Nonparametric Statististical Method For The Analysis Of Ordered...inventionjournals
This paper proposes a nonparametric statistical method for the analysis of repeated measures
that adjusts for the possibility of tied observation in the data which may be observations in time, space or
condition. The proposed method develops a test statistic for testing whether subjects are progressively
improving (or getting worse) in their experience over time, space or condition. The method is illustrated with
some data and shown to be at least as powerful as the Friedman’s two way analysis of variance test by ranks
even in the absence of any in-built ordering in the variable under study
Identify factors contributing increasing the use of POS in agricultural bank...inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Destructions and Progress: Removing Social Misconceptions on Environmental Is...inventionjournals
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Decision Management: The Dowsing Rod of ChangeMartijn Zoet
Business rules change at an increasing pace which is caused by rapidly changing customer requirements, legislation and policy. Simultaneously, a shift arises that demands an enhanced transparency with regard to the business operations of organizations. This shift forces organizations to demonstrate which business rules are applied in which situations. In order to be able to keep up with the high frequency of change and to comply with the transparency demands, the management of decisions and underlying business rules becomes increasingly important.
In this article, a classification scheme is presented which can be used to categorize the possible modifications with regard to information systems that execute decisions. Concerning these modifications, a distinction is made between the effort of the modification for the internal organization and the effort for a client.
Design of a Filtration System for a Small Scale Water Treatment Plant for a R...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
SWAREET is the last Project of Bakeri City Phase-1. The project is located on Opp. Smarana Apartments, Bakeri City, Close to prahladnagar, Vejalpur. The project is part of well developed Bakeri City which comprises of more than 5,000 residential units.
Just 5 minutes drive from Prahladnagar. Developing destinations like S.G. Road and Makarba are in close proximity of Bakeri City.
The proposed project consists of 530 well - designed 1, 1.5, 2 BHK Homes.
The project shall have well developed common amenities along with allotted parking space/s for two and four wheelers.The proposed residential units are spread over basement / parking and 5 floors.
A model for profit pattern mining based on genetic algorithmeSAT Journals
Abstract
Mining profit oriented patterns is a novel technique of association rule mining in data mining, which basically focuses on important issues related with business. As it is well known that every business aims to generate the profit and find the ways to improve the same. In earlier days association rule mining was used for market basket analysis and targeted only some of the business and commercial aspects. Afterwards the researchers started to aim the most prominent element of any business i.e. Profit, and determined the innovative way to generate the association rules based on profit. Profit oriented patterns mining approach combines the statistic based pattern mining with value-based decision making to generate those patterns with the maximum profit and some ways to generate recommenders for future strategy. To achieve the desired goal the traditional association rule mining alone is not effectual, so we combine the strength of genetic algorithm with association rule mining to enhance its capability. The study shows that Genetic Algorithm improves the effectiveness and efficiency of association rule mining outcome, since genetic algorithms are competent to handle the problems related with the uncertainty, multi-dimensional, non-differential, non-continuous, and non-parametrical, non-linearity constraint and multi-objective optimization problems. In this paper we apply the concept of profit pattern mining with genetic algorithm to generate profit oriented pattern which help out in future business expansion and fulfill the business objective.
Keywords: Data Mining, Association Rule Mining, Profit Pattern Mining, Genetic Algorithm
A model for profit pattern mining based on genetic algorithmeSAT Journals
Abstract
Mining profit oriented patterns is a novel technique of association rule mining in data mining, which basically focuses on important issues related with business. As it is well known that every business aims to generate the profit and find the ways to improve the same. In earlier days association rule mining was used for market basket analysis and targeted only some of the business and commercial aspects. Afterwards the researchers started to aim the most prominent element of any business i.e. Profit, and determined the innovative way to generate the association rules based on profit. Profit oriented patterns mining approach combines the statistic based pattern mining with value-based decision making to generate those patterns with the maximum profit and some ways to generate recommenders for future strategy. To achieve the desired goal the traditional association rule mining alone is not effectual, so we combine the strength of genetic algorithm with association rule mining to enhance its capability. The study shows that Genetic Algorithm improves the effectiveness and efficiency of association rule mining outcome, since genetic algorithms are competent to handle the problems related with the uncertainty, multi-dimensional, non-differential, non-continuous, and non-parametrical, non-linearity constraint and multi-objective optimization problems. In this paper we apply the concept of profit pattern mining with genetic algorithm to generate profit oriented pattern which help out in future business expansion and fulfill the business objective.
Keywords: Data Mining, Association Rule Mining, Profit Pattern Mining, Genetic Algorithm
Survey on Efficient Techniques of Text Miningvivatechijri
In the current era, with the advancement of technology, more and more data is available in digital
form. Among which, most of the data (approx. 85%) is in unstructured textual form. So it has become essential to
develop better techniques and algorithms to extract useful and interesting information from this large amount of
textual data. Text mining is process of extracting useful data from unstructured text. The algorithm used for text
mining has advantages and disadvantages. Moreover the issues in the field of text mining that affect the accuracy
and relevance of the results are identified.
Neighborhood search methods with moth optimization algorithm as a wrapper met...IJECEIAES
Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flame Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.
A Comparison between FPPSO and B&B Algorithm for Solving Integer Programming ...Editor IJCATR
Branch and Bound technique (B&B) is commonly used for intelligent search in finding a set of integer solutions within a space of interest. The corresponding binary tree structure provides a natural parallelism allowing concurrent evaluation of sub-problems using parallel computing technology. Flower pollination Algorithm is a recently-developed method in the field of computational intelligence. In this paper is presented an improved version of Flower pollination Meta-heuristic Algorithm, (FPPSO), for solving integer programming problems. The proposed algorithm combines the standard flower pollination algorithm (FP) with the particle swarm optimization (PSO) algorithm to improve the searching accuracy. Numerical results show that the FPPSO is able to obtain the optimal results in comparison to traditional methods (branch and bound) and other harmony search algorithms. However, the benefits of this proposed algorithm is in its ability to obtain the optimal solution within less computation, which save time in comparison with the branch and bound algorithm.Branch and bound, flower pollination Algorithm; meta-heuristics; optimization; the particle swarm optimization; integer programming.
MOCANAR: A MULTI-OBJECTIVE CUCKOO SEARCH ALGORITHM FOR NUMERIC ASSOCIATION RU...cscpconf
Extracting association rules from numeric features involves searching a very large search space. To
deal with this problem, in this paper a meta-heuristic algorithm is used that we have called
MOCANAR. The MOCANAR is a Pareto based multi-objective cuckoo search algorithm which
extracts high quality association rules from numeric datasets. The support, confidence,
interestingness and comprehensibility are the objectives that have been considered in the
MOCANAR. The MOCANAR extracts rules incrementally, in which, in each run of the algorithm, a
small number of high quality rules are made. In this paper, a comprehensive taxonomy of metaheuristic
algorithm have been presented. Using this taxonomy, we have decided to use a Cuckoo
Search algorithm because this algorithm is one of the most matured algorithms and also, it is simple
to use and easy to comprehend. In addition, until now, to our knowledge this method has not been
used as a multi-objective algorithm and has not been used in the association rule mining area. To
demonstrate the merit and associated benefits of the proposed methodology, the methodology has
been applied to a number of datasets and high quality results in terms of the objectives were
extracted
MOCANAR: A Multi-Objective Cuckoo Search Algorithm for Numeric Association Ru...csandit
Extracting association rules from numeric features
involves searching a very large search space. To
deal with this problem, in this paper a meta-heuris
tic algorithm is used that we have called
MOCANAR. The MOCANAR is a Pareto based multi-object
ive cuckoo search algorithm which
extracts high quality association rules from numeri
c datasets. The support, confidence,
interestingness and comprehensibility are the objec
tives that have been considered in the
MOCANAR. The MOCANAR extracts rules incrementally,
in which, in each run of the algorithm, a
small number of high quality rules are made. In thi
s paper, a comprehensive taxonomy of meta-
heuristic algorithm have been presented. Using this
taxonomy, we have decided to use a Cuckoo
Search algorithm because this algorithm is one of t
he most matured algorithms and also, it is simple
to use and easy to comprehend. In addition, until n
ow, to our knowledge this method has not been
used as a multi-objective algorithm and has not bee
n used in the association rule mining area. To
demonstrate the merit and associated benefits of th
e proposed methodology, the methodology has
been applied to a number of datasets and high quali
ty results in terms of the objectives were
extracted
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The Optimal Portfolio Based on the Bat Algorithm Research
1. International Journal of Engineering Science Invention
ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726
www.ijesi.org ||Volume 4 Issue 8|| August 2015 || PP.08-16
www.ijesi.org 8 | Page
The Optimal Portfolio Based on the Bat Algorithm Research
Jingjing Wang
Department of Mathematics Hunan University of Humanities, Science and TechnologyHunan, China
Abstract: The optimal portfolio model in recent years become a hot research topic in the field of economy, as
the scholars of the classical markowitz model, put forward the optimization model is closer to reality and more
complex, modeling is not difficult, difficult is how to solve the problem. Bat Algorithm (Bat Algorithm, BA) is a
kind of simulation by Yang Bat echolocation behavior of new type of intelligent optimization Algorithm is
proposed. The Algorithm gradually aroused people's great attention, and greater success in terms of application.
This article will use the bat algorithm to solve the problem, the optimal portfolio for the optimal portfolio model
is difficult to solve the general problem, manifests the bat algorithm in solving the optimization model of the role
of.
Keywords : The optimal portfolio; The bat algorithm; Variance; Multi-objective programming
I INTRODUCTION
As is known to all, the financial investment is not a crude words, it has gradually into our study, work
and life, we are also gradually will invest as a part of our lives, whether we are office workers, migrant workers or
big boss, will be conscious of the unconscious to touch this thing.
Now, however, the social rapid development, investment varieties and species are rich and colorful, have
divided according to the sort of investment without risk investment and investment risks, according to the
investment income, or risk division also has a lot of projects for us to choose, investment project is different,
different times, have to withstand risks and the resulting revenue is different, so, now the most important is how to
rationally, correct, reasonable and accurate.
Fast growth, investment in the classification of the lot, that we have been able to consciously choose a variety
of investment projects, according to the different emphasis on mutual combination, to achieve the best yield, and
the least risk, however, profit and risk coexist, in general, the higher the income, the higher the risk, income is
general, have to bear the risk is very few, so, how to select the optimal portfolio effectively is what we want to
solve the problem.
In this paper to select the optimal portfolio, based on the bat algorithm, establish a reasonable model, efficient
searching for different capital proportion of investment projects, in order to achieve maximum yields, as well as
the minimum risk coefficient.
1.1 Literature review
On the optimal portfolio, there are many scholars have carried on the thorough research. Shi-qing Miao
]2[
(2009) based on intelligent algorithm is studied the portfolio optimization model, the integrated application of
value at risk theory and optimization theory, the study of complex and changeable investment decision problems
in financial markets, established some portfolio research model based on different risk measures, and for the
design of the proposed model to solve the effective feasible intelligent algorithm. Chuan-ming Ding
]3[
, Jie-zhong
Zou
]4[
(2002) studied in complete financial markets, investors, the optimal combination of the stochastic model,
2. The Optimal Portfolio Based on the Bat …
www.ijesi.org 9 | Page
the model parameters for the constant coefficient, the utility function for ]),0[],,0( TBT the bounded
measurable function on the circumstances, it is concluded that the maximum utility function is a stochastic control
problem is the smooth solution of the corresponding HJB equation, proved to be the optimal strategy, and
feedback form the optimal portfolio strategy is presented. Jian-ying Xu
]5[
(2009), from the perspective of the
status quo and characteristics of online keyword advertising, introduces the main three online keyword
advertising providers, analyzes the importance of investment allocation between different platforms and the
research significance, combining the method of bayesian networks for three search engine platform set up
separate yield prediction model, and is widely used in the financial sector is introduced into the Markowitz
portfolio theory, put forward under the condition of different number of portfolio optimization model, based on
this, also from the view of the enterprise of advertisers set up an integrated management platform, unified the
previous research work. Xiao-wei Zhang
]6[
(2008) application of modern portfolio theory to establish insurance
company portfolio model, with a planning method to work out the optimal portfolio risk, the insurance company to
establish insurance company at the same time the utility function, it is concluded that the insurance company to
utility maximization of the optimal portfolio, and finally, the theoretical results compared with insurance
company actual portfolio analysis, put forward policy Suggestions. Liang-zhu Fan
]7[
(2005) first introduces the
basic concepts of portfolio theory, such as risk, such as income, decentralization, relevant, and gives three main
models of portfolio, markowitz model, model of single factor and multi-factor model, secondly the markowitz
mean - variance model put forward by the optimization Settings extends to transaction fees and don't allow
short-selling situation, when the final practice has proved that using genetic algorithm to solve the portfolio
optimization problem is very effective.
In recent years, our country launched the bat algorithm based on a series of application in all fields. Xiao-hua
Sheng
]8[
, Chun-ming
]9[
(2000) for the new heuristic intelligent algorithm bat algorithm of the limitation of
discrete production scheduling problem, use the bat algorithm recoding and initialization approach to solving
discrete production scheduling problem, based on the classical scheduling benchmark data to test, and compared
to more mature standard particle swarm optimization (pso) algorithm, the results show that the bat algorithm in
solving the problem of discrete production scheduling has better optimization performance, bat is verified the
feasibility and effectiveness of the algorithm to solve the discrete problem. Zhi-yong Li.
]10[
, Liang Ma
]11[
,
Hui-zhen Zhang
]12[
(2000) study the bat algorithm in the application of multiobjective multiple choice knapsack
problem, in view of the traditional multi-objective multiple choice knapsack optimization algorithm due to the
computational complexity is very high, it is difficult to obtain satisfactory solutions for problems such as, on the
basis of the bat algorithm, an improved bat algorithm used for multi target multiple choice knapsack problem, and
algorithm design, first introduced the inertial factor effect on the speed of the bat, redefined the update equations
of the speed of the bat, is used to improve the convergence speed of the algorithm, and then gives the bat update
the rules of individuals and groups, guide the bats fly to Pareto, finally the simulation results show that compared
with particle swarm optimization (pso) algorithm, the bat algorithm can find the same number of faster Pareto,
manifests the bat algorithm to solve the problem and the feasibility and effectiveness of the bat algorithm
performance superiority, expand the application field of the bat algorithm.
1.2 The optimal portfolio
Portfolio, which is held by the investors or financial institutions of stocks, bonds, derivatives and other
collections. The purpose of the portfolio is to spread risk.
]13[
Alleged portfolio theory, it is a combination of
several different types of securities portfolio, its earnings are weighted average of these securities gains, however,
its risk is not these securities risk weighted average of the risk, so the portfolio can help reduce the systemic
risk.
]14[
3. The Optimal Portfolio Based on the Bat …
www.ijesi.org 10 | Page
Based on the principle of diversification, the need to diversify into different capital investment projects; On
specific investment projects, but also need the assets as diversified distribution, make the investment proportion.
Bear in mind that any optimal portfolio, must be spread risk.
]13[
Your income is more and more long, will be in
the hands of funds dispersed in different fields is absolutely the right thing to do.
The task of this paper is required to establish an optimal portfolio model, to "profit maximization".
II. THE BAT ALGORITHM
Bat algorithm that is based on micro bats (microbats) echolocation, the basis of different pulse emissivity and
loudness.
Bat algorithm of the basic definition, the echo-location in bats is ideal, can be summarized as follows:
Each virtual bats have random speed of iv in position ix (solving), at the same time the bat with different
frequency or wavelength, loudness, iA and r pulse firing rate. Bats hunting and found its prey, it changes,
loudness, and emissivity of the pulse frequency, the choice of optimal solution, until the target to stop or
conditions are met. This is essentially a tuning technique was used to control the dynamic behavior of the bat
group, balance adjustment algorithm related parameters, in order to obtain the bat algorithm of the optimal.
According to Yang (2010),The new solution speed
t
ix and
t
iv The update equation:
)( minmaxmin ffffi ,
i
t
i
t
i
t
i fxxvv *
1
,
t
i
t
i
t
i vxx 1
,
Among them, the number is evenly distributed.
*x is now to find the optimal solution.
A and r should be in the iterative transformation:
t
i
t
i AA 1
,
)]exp(1[01
trr i
t
i
,
Among them, 10 and 0 that are constant.
III. THE OPTIMAL PORTFOLIO BASED ON THE BAT ALGORITHM RESEARCH
3.1 symbolic description
m says number for venture capital projects
n says data for risk investment project of the month
0x says the proportion of funds for risk-free investment projects
ix says to the first item has the proportion of funds risk investment projects
jy0 is expressed as risk-free investment funds yield in the j months
4. The Optimal Portfolio Based on the Bat …
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ijy says as the first i has a risk investment in the first j months return on capital
0y is expressed as a risk-free investment investment projects in j months average yield
iy is expressed as the first i a venture capital investment projects in j months average yield
0z is expressed as risk-free investment project risk coefficient
iz says as the i has the risk coefficient of risk investment projects
a says total net benefits for the portfolio investment income
b says to existing total portfolio risk
3.2 data processing
(1) data into
Because there was no risk investment risk coefficient is 0, so might as well set investment risk-free investment
projects of capital ratio is 0x , i have set up investment risk investment projects of capital ratio is ix , might as
well set a total of m risk investment project, so the value of ix , i to m,,2,1 .
Same as above, also might as well set j risk-free investment in the first months of return on capital is jy0 , set
up the first i a risky investment projects in the first j months of capital return of ijy , ijy in i for the types of
values, given the m,,2,1 , ijy j values for n,,2,1 .
(2) data correlation
For mxxxx ,,,, 210 , 0x to the proportion of funds investment risk-free investment projects,
mxxx ,,, 21 said as the first i investment proportion of funds in risky investment project, so we can draw the
following formula:
10 ix
(Including mi ,,2,1,0 )
)1(1
1
1
210
m
i
im xxxxx
(3) the yield data processing
According to different risk investment projects, and by the corresponding risk coefficient is not necessarily
the same, so might as well put the first iz of risky investment project investment risk coefficient for iy , here,
because every ix has a iz counterpart, so the values for i m,,2,1 . (because there was no risk investment
5. The Optimal Portfolio Based on the Bat …
www.ijesi.org 12 | Page
risk coefficient is zero, in order to later forms of calculation can be unified, might as well also set a risk-free
investment risk coefficient for the 0z , 00 z ), for the determination of numerical iz ),,2,1( mi , can
adopt the form of analog variance, first using i a risky investment projects in the first j yields
ijy mi ,,2,1( , ),,2,1 nj months of funds, and given the first i a venture capital investment
projects in j months average yield of iy , iy 's expression is:
n
yyy
y inii
i
21
(Including mi ,,2,1 )
In addition, when 0i , namely the investment projects as risk-free investment projects, to remember the
risk-free investment projects in the first j months funds yields for jy0 ),,2,1( nj , to calculate the
average yield of j months 0y , the computational expressions of 0y as follows :
n
yyy
y n00201
0
For a specific myyy ,,, 21 , can also be given to the corresponding expressions:
n
yyy
y
n
yyy
y
n
yyy
y
mnmm
m
n
n
21
22221
2
11211
1
Next, using variance principle, there are risks of venture capital project investment item first i coefficient of
iz expression :
n
yyyyyy
z iiniiii
i
])()()[( 22
2
2
1
(Including mi ,,2,1 )
For a specific mzzz ,,, 21 , can also be given to the corresponding expressions:
6. The Optimal Portfolio Based on the Bat …
www.ijesi.org 13 | Page
n
yyyyyy
z
n
yyyyyy
z
n
yyyyyy
z
mmnmmmm
m
n
n
])()()[(
])()()[(
])()()[(
22
2
2
1
2
22
2
222
2
221
2
2
11
2
112
2
111
1
3.3 model is established in this paper
Sets the total net benefits of portfolio investment income to a , a and ix ),,2,1,0( mi and
iy ),,2,1,0( mi between calculation formula:
1
1
221100
m
i
iimm yxyxyxyxyxa
For a the bigger the better, namely:
amax
Set the total risk for the portfolio investment exist b , b and correlation between ix ),,2,1,0( mi and
iz ),,2,1,0( mi :
1
1
22
2
2
21
2
10
2
0
m
i
iimm zxzxzxzxzxb
For b as small as possible, i.e.,
bmin
To calculate when the greater the a , b more hours investment fund proportion mxxxx ,,,, 210 .
3.4 study the optimal portfolio model
In order to verify the advantages and disadvantages of the algorithm, we introduce the concrete data, to realize
the optimal portfolio model establishment and solving, specific four projects for six months the yield data is as
follows:
The first
month
the second
month
the third
month
the fourth
month
the sixth
month
the fifth
month
The first item 10.0 10.0 10.0 10.0 10.0 10.0
The second
project
9.5 12.0 8.0 17.0 11.0 9.5
The third item 13.0 15.0 13.5 17.0 14.5 15.0
The fourth item 19.5 14.5 10.5 11.5 15.0 18.0
7. The Optimal Portfolio Based on the Bat …
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(in %)
Step 1: data translation
0.1001 y 0.1002 y 0.1003 y 0.1004 y 0.1005 y 0.1006 y
5.911 y 0.1212 y 0.813 y 0.1714 y 0.1115 y 5.916 y
0.1321 y 0.1522 y 5.1323 y 0.1724 y 5.1425 y 0.1526 y
5.1931 y 5.1432 y 5.1033 y 5.1134 y 0.1535 y 0.1836 y
By observation of the data, we can find that the first project for risk-free investment projects, the second, three,
four projects for risky investments.
Step 2: basic data preprocessing
00.100 y 17.111 y 67.142 y 83.143 y
0000.00 z 5833.121 z 4583.22 z 4583.153 z
Step 3: through drawing and data analysis
By operation, drawing, we can get the first i investment investment projects in j months average yield of
iy )3,2,1,0( i section i and investment has a risk investment project risk coefficient of
iz )3,2,1,0( i intuitive relationship as shown in the figure below:
Second, we can also use the line chart more clearly show the first i investment projects of investment in
j months average yield of iy section i and investment has a risk investment projects, the relationship between
the risk coefficient of iz specific as shown in the figure below:
8. The Optimal Portfolio Based on the Bat …
www.ijesi.org 15 | Page
From above, we may direct observations, the greater the income, the higher the risk. However, different
investment returns and risk of function is not entirely a function, more or less will have certain amplitude
fluctuations, so, we can according to this feature, using the definition and nature of the bat algorithm, to calculate
the optimum operation of the portfolio.
Step 4: based on the bat algorithm to calculate
Through calculation of the bat algorithm further, we may safely draw it is concluded that the optimal portfolio
model based on the bat algorithm, namely the investment proportion of funds of the project 4321 ,,, xxxx :
25.00 x 30.01 x 10.02 x 35.03 x
Step 5: the total earnings
Based on previous funds based on the bat algorithm calculate portfolio proportion, calculate the total net
benefits for its portfolio income 51.12a .
Step 6: the total risk is calculated
Based on previous funds based on the bat algorithm calculate portfolio proportion, calculate the total risk of
portfolio investment 0507.3b .
IV. SUMMARY
Based on study of portfolio bat algorithm, we take the comprehensive analysis on the nature of the project at
different time points to draw the proportion of the investment projects in capital.
First of all, we will be modeled the data collected, in order to accurately the data and model is very good, and
not cause not form a complete set and as a result of the data is not accurate. Among them, in order to make the
model more representative, we let the data is general, respectively adopt a risk-free investment projects, with
9. The Optimal Portfolio Based on the Bat …
www.ijesi.org 16 | Page
three risky investment projects, a total of four projects, portfolio selection within six months of return on capital.
Secondly, data preprocessing, we use average algorithm, return on capital in different period to determine the
overall net benefits of the investment project, applying the idea of variance to capital yield per month to uniform
the total risk.
We use a given beforehand good portfolio model based on the bat algorithm, the data in, get the optimal
portfolio based on the bat algorithm, namely the different investment projects by the effort and money accounted
for the proportion of the overall, and carries on the analysis, the feasibility and necessity of the results.
Judging from the results of empirical study, this model is feasible, we can through the project previous returns
to uniform the project overall yield and risk coefficient, through the model, it is concluded that the future of the
project investment amount of the percentage of the total, through the deductive reasoning, and carries on the
analysis, the research team, the result is ideal, successful based on the idea of the bat algorithm the optimal
portfolio based on the bat algorithm is obtained.
V. ACKNOWLEDGEMENTS:
This research is supported by scientific research fund of Hunan University of Humanities Science and
Technology (2014QN04).
REFERENCES:
[1] http://zh.wikipedia.org/wiki/ # cite_ref bat algorithm - 3
[2] Shi-qing Miao. The portfolio optimization model based on intelligent algorithm research. Ningxia, ningxia university, 2009
[3] Chuan-ming Ding. Research on the optimal portfolio model. Changsha: central south university railway campus research
mathematical finance research, 2002
[4] Jie-zhong Zou. Research on the optimal portfolio model. Changsha: central south university railway campus research mathematical
finance research, 2002
[5] Jian-ying Xu. Online keyword advertising for the optimal portfolio research. Shanghai: Shanghai jiaotong university, 2009
[6] Xiao-wei Zhang. The optimal portfolio of insurance funds research and evaluation. Nanjing: nanjing university of finance and
economics, 2008
[7] Liang-zhu Fan. China's securities market the forehead is the optimal portfolio selection research. Wuhan: wuhan university, 2005
[8] Xiao-hua Sheng. Bat algorithm in the application of PFSP scheduling research. Shanghai: Shanghai university of science and
technology, 2000
[9] Chun-ming Ye. Bat algorithm in the application of PFSP scheduling research. Shanghai: Shanghai university of science and
technology, 2000
[10] Zhi-yong Li. The bat algorithm in the application of multiobjective multiple choice knapsack problem. Shanghai: Shanghai university of
science and technology, 2000
[11] Liang Ma. The bat algorithm in the application of multiobjective multiple choice knapsack problem. Shanghai: Shanghai university of
science and technology, 2000
[12] Hui-zhen Zhang. Bat algorithm in the application of multiobjective multiple choice knapsack problem. Shanghai: Shanghai university
of science and technology, 2000
[13] http://baike.baidu.com/view/194522.htm? Fr = Aladdin
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