Multi-criteria Decision Analysis for Customization of Estimation by Analogy Method
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The document discusses using multi-criteria decision analysis to customize estimation by analogy (EBA) methods. It proposes using the AQUA+ EBA method, which supports attributes weighting and selection. It presents a decision-centric process model for customizing EBA and defines a multi-criteria decision problem to determine the best attributes weighting heuristic for different datasets based on criteria like MMRE, prediction accuracy, and strength. It demonstrates the approach on sample datasets and identifies Pareto optimal solutions and clusters.
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Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/analyzing-the-solutions-of-dea-through-information-visualization-and-data-mining-techniques-smartdea-framework/
Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the pro-posed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.
Analyzing the solutions of DEA through information visualization and data min...
Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, Smart DEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for bench marking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.
http://research.sabanciuniv.edu.
The pertinent single-attribute-based classifier for small datasets classific...
Classifying a dataset using machine learning algorithms can be a big challenge when the target is a small dataset. The OneR classifier can be used for such cases due to its simplicity and efficiency. In this paper, we revealed the power of a single attribute by introducing the pertinent single-attributebased-heterogeneity-ratio classifier (SAB-HR) that used a pertinent attribute to classify small datasets. The SAB-HR’s used feature selection method, which used the Heterogeneity-Ratio (H-Ratio) measure to identify the most homogeneous attribute among the other attributes in the set. Our empirical results on 12 benchmark datasets from a UCI machine learning repository showed that the SAB-HR classifier significantly outperformed the classical OneR classifier for small datasets. In addition, using the H-Ratio as a feature selection criterion for selecting the single attribute was more effectual than other traditional criteria, such as Information Gain (IG) and Gain Ratio (GR).
Optimal rule set generation using pso algorithm
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
MAXIMUM CORRENTROPY BASED DICTIONARY LEARNING FOR PHYSICAL ACTIVITY RECOGNITI...
This document proposes a correntropy induced dictionary pair learning framework for physical activity recognition using wearable sensors. It begins with an introduction to physical activity recognition and related work. It then presents the proposed methodology, which consists of two stages: data processing and recognition. The recognition stage involves jointly learning a synthesis dictionary and analysis dictionary based on the maximum correntropy criterion. This is done using an alternating direction method of multipliers combined with an iteratively reweighted method to solve the non-convex objective function. The framework is validated on physical activity recognition and intensity estimation tasks using a publicly available dataset. Experimental results show the correntropy induced dictionary learning approach achieves high accuracy using simple features and is competitive with other methods requiring prior knowledge
Maximum Correntropy Based Dictionary Learning Framework for Physical Activity...
Due to its symbolic role in ubiquitous health monitoring,
physical activity recognition with wearable body sensors has been in the
limelight in both research and industrial communities. Physical activity
recognition is difficult due to the inherent complexity involved with different
walking styles and human body movements. Thus we present a
correntropy induced dictionary pair learning framework to achieve this
recognition. Our algorithm for this framework jointly learns a synthesis
dictionary and an analysis dictionary in order to simultaneously perform
signal representation and classification once the time-domain features
have been extracted. In particular, the dictionary pair learning algorithm
is developed based on the maximum correntropy criterion, which
is much more insensitive to outliers. In order to develop a more tractable
and practical approach, we employ a combination of alternating direction
method of multipliers and an iteratively reweighted method to approximately
minimize the objective function. We validate the effectiveness of
our proposed model by employing it on an activity recognition problem
and an intensity estimation problem, both of which include a large number
of physical activities from the recently released PAMAP2 dataset.
Experimental results indicate that classifiers built using this correntropy
induced dictionary learning based framework achieve high accuracy by
using simple features, and that this approach gives results competitive
with classical systems built upon features with prior knowledge.
DIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docx
DIRECTIONS: READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE ITS CONTENT. PLEASE CITE ALL REFERENCES
Katie Kessler
Unit 2 Discussion 1
Top of Form
The word “noir” is used to remember the scaling of measurement in psychology (Embretson, 2004). In short, the letters stand for nominal, ordinal, interval and ratio (Embretson, 2004). To give a brief introduction of what each scale measures, “nominal is the simplest way to measure” because it focuses on categorizing measurements on a scale of category, according to Embretson (2004). An example of nominal is eye color. “Ordinal measures in terms of ranking, interval measures scores of tests that focus on unobservable mental functioning and ratio focuses on measuring activities in the physical world, such as someone’s running time” (Embretson, 2004). With different scales of measurement, there are two methods to compare sets of data. These include norm-referenced and criterion-referenced testing. According to Embretson (2004) norm-referenced testing “yields information on a testtaker’s standing or ranking relative to some comparison group of testtakers.” In other words, it focuses on the performance of peers. Criterion-referenced testing is a little different because it focuses on examining individual’s scores to a set standard (Embretson, 2004).
The ability for ordinal measurement scale to be utilized on a standardized test as a norm-referenced test is high since an ordinal scale is based upon ranking and norm-referenced testing gathers information on the examinees ranking compared to a group of testtakers. For example, a study conducted on decision making with the use of ordinal variables states that ordinal measurement scales has the ability to be utilized by norm-referenced testing (Barua, Kademane, Das, Gubbiyappa, Verma, & Al-Dubai, 2014).On the other hand, ordinal scaling would not be a strong measurement for criterion-referenced testing because it focuses on the ranking rather than the measurement of the scores to be close to a set standard.
Ratio scaling directs its focus on measuring objects and activities in the physical world which would be beneficial for criterion-referenced testing instead of norm-referenced testing. Imagine a marathon runner who was trying to beat the world’s fastest time running a marathon. Criterion-referenced testing allows the runner to be aware of the set standard the marathon runner needs to beat to be the best and set a new standard. Norm-referenced testing would not be as useful because the marathon runner would not have the standard measurement he or she needs to beat. However, the marathon runner would be aware of the relative time he or she needs to beat to be the best. That is not as helpful as the criterion-referenced testing because runners need an exact number instead of a relative number in comparison to other runners.
Norm-referenced data would be collected by “the standards relative to a group, such as means and standard deviation.
Webinar slides how to reduce sample size ethically and responsibly
[Webinar] How to reduce sample size...ethically and responsibly | In this free webinar, you will learn various design strategies to help reduce the sample size of your study in an ethical and responsible manner. Practical examples will be used throughout.
Paper sharing_Explaining Data-Driven Decisions made by AI Systems_The Counter...
MIS QUARTERLY
Fernandez, Carlos & Provost, Foster & Han, Xintian
Presenter ：CHEN,YOU-SHENG
(Shane) 2022/12/15
A study on rough set theory based
In the present day huge amount of data is generated in every minute and transferred frequently. Although
the data is sometimes static but most commonly it is dynamic and transactional. New data that is being
generated is getting constantly added to the old/existing data. To discover the knowledge from this
incremental data, one approach is to run the algorithm repeatedly for the modified data sets which is time
consuming. Again to analyze the datasets properly, construction of efficient classifier model is necessary.
The objective of developing such a classifier is to classify unlabeled dataset into appropriate classes. The
paper proposes a dimension reduction algorithm that can be applied in dynamic environment for
generation of reduced attribute set as dynamic reduct, and an optimization algorithm which uses the
reduct and build up the corresponding classification system. The method analyzes the new dataset, when it
becomes available, and modifies the reduct accordingly to fit the entire dataset and from the entire data
set, interesting optimal classification rule sets are generated. The concepts of discernibility relation,
attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of
dynamic reduct set, and optimal classification rules are selected using PSO method, which not only
reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed
method has been applied on some benchmark dataset collected from the UCI repository and dynamic
reduct is computed, and from the reduct optimal classification rules are also generated. Experimental
result shows the efficiency of the proposed method.
Multimodal Biometrics Recognition by Dimensionality Diminution Method
Multimodal biometric system utilizes two or more character modalities, e.g., face, ear, and fingerprint,
Signature, plamprint to improve the recognition accuracy of conventional unimodal methods. We propose a new
dimensionality reduction method called Dimension Diminish Projection (DDP) in this paper. DDP can not only
preserve local information by capturing the intra-modal geometry, but also extract between-class relevant
structures for classification effectively. Experimental results show that our proposed method performs better
than other algorithms including PCA, LDA and MFA.
Heart disease classification
This document discusses a project that uses machine learning algorithms to predict potential heart diseases. The project uses a dataset with 13 features and applies algorithms like K-Nearest Neighbors Classifier and Support Vector Classifier, with and without PCA. The K-Nearest Neighbors Classifier achieved the best accuracy score of 87% at predicting heart disease based on the dataset.
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DIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docx
DIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docx
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Independent Study - College of Wooster Research (2023-2024) FDI, Culture, Glo...AntoniaOwensDetwiler
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.一比一原版(UCSB毕业证)圣芭芭拉分校毕业证如何办理
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Independent Study - College of Wooster Research (2023-2024)
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.
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Multi-criteria Decision Analysis for Customization of Estimation by Analogy Method
- 1. Multi-criteria Decision Analysis for Customization of Estimation by Analogy Method AQUA + Jingzhou Li Guenther Ruhe University of Calgary, Canada PROMISE’08, May 13, 2008
- 3. /14 Prediction accuracy distribution 1. Proposed EBA method AQUA + —Architecture Data set for AQUA + AQUA Existing EBA Predicting Phase2 Effort estimates Objects under estimation Learning Phase1
- 5. 2. Decision-centric process model of EBA /14 Processed Historical Data Raw Historical Data D8. Determining closest analogs D2. Dealing with missing values D1. Impact analysis of missing values D7. Retrieving analogs Objects Under Estimation Effort Estimates D9. Analogy adaptation D11. Comparing EBA methods in general D10. Choosing evaluation criteria D6. Determining similarity measures D3. Object selection D5. Attribute weighting & selection D4. Discretization of attributes
- 6. /14 EBA ( DB ) = C ( D , DB , Ch ) Data set type 1 Data set type 2 Data set type k …… Classification according to characteristics of the data sets S i.j for D i ? 3. Customization of EBA — why? D = { D 1 , D 2 , …, D 11 }, D i = { S i.j | solution alternatives of task D i } DB: a historical data set for EBA Ch: a set of characteristics describing DB Customization 1 Customization 2 Customization k
- 7. /14 New Data Set Which heuristic should be used? 4. Customization of EBA — how? Empirical knowledge gained from empirical studies.
- 10. /14 7. Data sets Data Sets #Objects #Attributes %Missing Values %Non-Quantitative Attributes Source USP05-RQ 121 14 2.54 71 Li et al., 2005 USP05-FT 76 14 6.8 71 Li et al., 2005 ISBSG04-2 158 24 27.24 63 ISBSG, 2004 Kem87 15 5 0 40 Kemerer et al., 1987 Mends03 34 6 0 0 Mendes et al., 2003 Desh89 81 10 0.006 20 Shepperd et al., 1997
- 11. /14 8. Decision Analysis Using ELECTRE Outranking graph and analysis data for Desh89 (an example) Heuristic MMRE Pred(0.25) H0 0.62 0.44 H1 0.61 0.44 H3 0.6 0.42 H4 0.59 0.42 CfsSubset (Cfs) 0.52 0.4 Wrapper (Wp) 0.66 0.43
- 15. /14 Discussion and questions?
Editor's Notes
- Slide 11: 4 Heuristics as a header for the lower Introduce a simplified formulae exlaing how the coefficients were calculated. - Be prepared for the following questions: (1) Are there alternatives to using RSA to determine the importance of the attributes? (2) What is the overall effort of the method(s) (3) Wham means the name AQUA? (4) When do you recommend apply the method? (and when better not?) (5) Needs the learning be done after each new prediction (data point)??