This document discusses different types of machine learning problems including association rule learning and classification. Association rule learning aims to discover relationships between variables in large datasets, such as customers that buy onions and potatoes also tend to buy hamburgers. Classification involves identifying which category a new observation belongs to based on a training set where category membership is known, such as recognizing characters, identifying faces, or diagnosing illnesses. Several common machine learning algorithms used for classification are also listed.
Patterns that only occur in objects belonging to a
single class are called Jumping Emerging Patterns (JEP). JEP
based Classifiers are considered one of the successful classification
systems. Due to its comprehensibility, simplicity and strong
differentiating abilities JEPs have captured significant recognition.
However, discovery of JEPs in a large pattern space is normally a
time consuming and challenging task because of their exponential
behaviour. In this work a novel method based on genetic
algorithm (GA) is proposed to discover JEPs in large pattern
space. Since the complexity of GA is lower than other algorithms,
so we have combined the power of JEPs and GA to find high
quality JEPs from datasets to improve performance of
classification system. Our proposed method explores a set of high
quality JEPs from pattern search space unlike other methods in
literature that compute complete set of JEPs, Large numbers of
duplicate and redundant JEPs are filtered out during their
discovery process. Experimental results show that our proposed
Genetic-JEPs are effective and accurate for classification of a
variety of data sets and in general achieve higher accuracy than
other standard classifiers.
Survey on Various Classification Techniques in Data Miningijsrd.com
Dynamic Classification is an information mining (machine learning) strategy used to anticipate bunch participation for information cases. In this paper, we show the essential arrangement systems. A few significant sorts of arrangement technique including induction, Bayesian networks, k-nearest neighbor classifier, case-based reasoning, genetic algorithm and fuzzy logic techniques. The objective of this review is to give a complete audit of distinctive characterization procedures in information mining.
Patterns that only occur in objects belonging to a
single class are called Jumping Emerging Patterns (JEP). JEP
based Classifiers are considered one of the successful classification
systems. Due to its comprehensibility, simplicity and strong
differentiating abilities JEPs have captured significant recognition.
However, discovery of JEPs in a large pattern space is normally a
time consuming and challenging task because of their exponential
behaviour. In this work a novel method based on genetic
algorithm (GA) is proposed to discover JEPs in large pattern
space. Since the complexity of GA is lower than other algorithms,
so we have combined the power of JEPs and GA to find high
quality JEPs from datasets to improve performance of
classification system. Our proposed method explores a set of high
quality JEPs from pattern search space unlike other methods in
literature that compute complete set of JEPs, Large numbers of
duplicate and redundant JEPs are filtered out during their
discovery process. Experimental results show that our proposed
Genetic-JEPs are effective and accurate for classification of a
variety of data sets and in general achieve higher accuracy than
other standard classifiers.
Survey on Various Classification Techniques in Data Miningijsrd.com
Dynamic Classification is an information mining (machine learning) strategy used to anticipate bunch participation for information cases. In this paper, we show the essential arrangement systems. A few significant sorts of arrangement technique including induction, Bayesian networks, k-nearest neighbor classifier, case-based reasoning, genetic algorithm and fuzzy logic techniques. The objective of this review is to give a complete audit of distinctive characterization procedures in information mining.
Feature selection on boolean symbolic objectsijcsity
With the boom in IT technology, the data sets used in
application are more and more larger and are
described by a huge number of attributes, therefore, the feature selection become an important discipline in
Knowle
dge discovery and data mining, allowing the experts
to select the most relevant features to impr
ove
the quality of their studies and to reduce the time processing of their algorithm. In addition to that, the data
used by the applications become richer. They are now represented by a set of complex and structured
objects, instead of simple numerical ma
trixes. The purpose of
our
algorithm is to do feature selection on
rich data,
called Boolean Symbolic Objects
(BSOs)
. These objects are desc
ribed by multivalued features.
The
BSOs
are considered as higher level units which can model complex data, such as c
luster of
individuals, aggregated data or taxonomies. In this paper we will introduce a new feature selection
criterion for
BSOs
, and we will explain
how we improved its complexity.
A COMPARATIVE STUDY OF FEATURE SELECTION METHODSijnlc
Text analysis has been attracting increasing attention in this data era. Selecting effective features from datasets is a particular important part in text classification studies. Feature selection excludes irrelevant features from the classification task, reduces the dimensionality of a dataset, and improves the accuracy and performance of identification. So far, so many feature selection methods have been proposed, however,
it remains unclear which method is the most effective in practice. This article focuses on evaluating and comparing the available feature selection methods in general versatility regarding authorship attribution problems and tries to identify which method is the most effective. The discussions on general versatility of feature selection methods and its connection in selecting the appropriate features for varying data were
done. In addition, different languages, different types of features, different systems for calculating the accuracy of SVM (support vector machine), and different criteria for determining the rank of feature selection methods were used to measure the general versatility of these methods together. The analysis
results indicate the best feature selection method is different for each dataset; however, some methods can always extract useful information to discriminate the classes. The chi-square was proved to be a better method overall.
Overview of Quantitative research by Prof Rajbir Singh.Dr Rajesh Verma
In sciences we conduct research in order to determine the acceptability of hypotheses derived from theories. Having selected a certain hypothesis which seems important in a certain theory, we collect empirical data which should yield direct information on the acceptability of that hypothesis. Our decision about the meaning of the data may lead us to retain, revise, or reject the hypothesis and even the theory which was its source
In AI We Trust? Ethics & Bias in Machine Learning
Hosted by Seven Peaks Ventures and Fast Forward Labs
September 2016
Decision-making about critical life stages (college admissions, creditworthiness, employability, jail sentencing) is rapidly becoming centralized and predicted by automated systems like machine learning models. As Data Scientists, the creators of those models, how do we take responsibility for those decisions? How do we define our goals, and how do we measure the effect? As this presents a unique opportunity and risk to businesses, we all become invested in the answers to these questions. Here, I focus on the tactical elements of measuring fairness as well as the forward-looking concerns and opportunities this paradigmatic change presents.
https://www.eventbrite.com/e/in-ai-we-trust-ethics-bias-in-machine-learning-tickets-26313436196#
Feature selection on boolean symbolic objectsijcsity
With the boom in IT technology, the data sets used in
application are more and more larger and are
described by a huge number of attributes, therefore, the feature selection become an important discipline in
Knowle
dge discovery and data mining, allowing the experts
to select the most relevant features to impr
ove
the quality of their studies and to reduce the time processing of their algorithm. In addition to that, the data
used by the applications become richer. They are now represented by a set of complex and structured
objects, instead of simple numerical ma
trixes. The purpose of
our
algorithm is to do feature selection on
rich data,
called Boolean Symbolic Objects
(BSOs)
. These objects are desc
ribed by multivalued features.
The
BSOs
are considered as higher level units which can model complex data, such as c
luster of
individuals, aggregated data or taxonomies. In this paper we will introduce a new feature selection
criterion for
BSOs
, and we will explain
how we improved its complexity.
A COMPARATIVE STUDY OF FEATURE SELECTION METHODSijnlc
Text analysis has been attracting increasing attention in this data era. Selecting effective features from datasets is a particular important part in text classification studies. Feature selection excludes irrelevant features from the classification task, reduces the dimensionality of a dataset, and improves the accuracy and performance of identification. So far, so many feature selection methods have been proposed, however,
it remains unclear which method is the most effective in practice. This article focuses on evaluating and comparing the available feature selection methods in general versatility regarding authorship attribution problems and tries to identify which method is the most effective. The discussions on general versatility of feature selection methods and its connection in selecting the appropriate features for varying data were
done. In addition, different languages, different types of features, different systems for calculating the accuracy of SVM (support vector machine), and different criteria for determining the rank of feature selection methods were used to measure the general versatility of these methods together. The analysis
results indicate the best feature selection method is different for each dataset; however, some methods can always extract useful information to discriminate the classes. The chi-square was proved to be a better method overall.
Overview of Quantitative research by Prof Rajbir Singh.Dr Rajesh Verma
In sciences we conduct research in order to determine the acceptability of hypotheses derived from theories. Having selected a certain hypothesis which seems important in a certain theory, we collect empirical data which should yield direct information on the acceptability of that hypothesis. Our decision about the meaning of the data may lead us to retain, revise, or reject the hypothesis and even the theory which was its source
In AI We Trust? Ethics & Bias in Machine Learning
Hosted by Seven Peaks Ventures and Fast Forward Labs
September 2016
Decision-making about critical life stages (college admissions, creditworthiness, employability, jail sentencing) is rapidly becoming centralized and predicted by automated systems like machine learning models. As Data Scientists, the creators of those models, how do we take responsibility for those decisions? How do we define our goals, and how do we measure the effect? As this presents a unique opportunity and risk to businesses, we all become invested in the answers to these questions. Here, I focus on the tactical elements of measuring fairness as well as the forward-looking concerns and opportunities this paradigmatic change presents.
https://www.eventbrite.com/e/in-ai-we-trust-ethics-bias-in-machine-learning-tickets-26313436196#
Workshop given to the staff for PhD and Masters Topic Selection in the area of Big Data, Data Science and Machine Learning. It has many interactive online demos to understanding on NLP social media analysis like sentiment analysis , topic modeling , language detection and intent detection. Some of the basic concept about classification and regression and clustering with interactive worksheets. Finally , hands-on machine learning models and comparisons in WEKA tool kit with case study of cars and diabetic patient data.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
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Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2. Unit of observation
• By a unit of observation we mean the smallest
entity with measured properties of interest for a
study.
Examples
• A person, an object or a thing
• A time point
• A geographic region
• A measurement
Sometimes, units of observation are combined to
form units such as person-years.
Dr.Ranjitha M ; Kristu Jayanti College
3. Examples/Instances and features
• Datasets that store the units of observation and their
properties can be imagined as collections of data consisting
of the following:
Examples
• An “example” is an instance of the unit of observation for
which properties have been recorded.
• An “example” is also referred to as an “instance”, or “case”
or “record.” (It may be noted that
the word “example” has been used here in a technical sense.)
Features
• A “feature” is a recorded property or a characteristic of
examples. It is also referred to as “attribute”, or “variable”
or “feature.”
Dr.Ranjitha M ; Kristu Jayanti College
4. Examples for “examples” and
“features”
1. Cancer detection
Consider the problem of developing an algorithm for
detecting cancer. In this study we note the following.
(a) The units of observation are the patients.
(b) The examples are members of a sample of cancer patients.
(c) The following attributes of the patients may be chosen as
the features:
• gender
• age
• blood pressure
• the findings of the pathology report after a biopsy
Dr.Ranjitha M ; Kristu Jayanti College
5. 2. Pet selection
Suppose we want to predict the type of pet a
person will choose.
(a) The units are the persons.
(b) The examples are members of a sample of
persons who own pets.
(c) The features might include age, home region,
family income, etc. of persons who own pets.
Dr.Ranjitha M ; Kristu Jayanti College
6. Dr.Ranjitha M ; Kristu Jayanti College
Example for “examples” and “features” collected in a matrix format (data
relates to automobiles and their features). Examples and features are
generally collected in a “matrix format”. Figure shows such a data
set.
7. 3. Spam e-mail
Let it be required to build a learning algorithm
to identify spam e-mail.
(a) The unit of observation could be an e-mail
messages.
(b) The examples would be specific messages.
(c) The features might consist of the words used
in the messages.
Dr.Ranjitha M ; Kristu Jayanti College
8. Different forms of data
1. Numeric data
If a feature represents a characteristic measured in numbers, it is
called a numeric feature.
2. Categorical or nominal
A categorical feature is an attribute that can take on one of a limited,
and usually fixed, number of possible values on the basis of some
qualitative property. A categorical feature is also called a nominal
feature.
3. Ordinal data
This denotes a nominal variable with categories falling in an ordered
list.
Examples include
• clothing sizes such as small, medium, and large, or
• a measurement of customer satisfaction
on a scale from “not at all happy” to “very happy.”
Dr.Ranjitha M ; Kristu Jayanti College
9. Examples
• In the data given in previous slide, the
features “year”, “price” and “mileage” are
numeric
• and the features“model”, “color” and
“transmission” are categorical.
Dr.Ranjitha M ; Kristu Jayanti College
10. General classes of machine learning
problems
Learning associations
1. Association rule learning
• Association rule learning is a machine learning method for
discovering interesting relations, called “association rules”, between
variables in large databases using some measures of
“interestingness”.
Example
• Consider a supermarket chain. The management of the chain is
interested in knowing whether there are any patterns in the
purchases of products by customers like the following:
• “If a customer buys onions and potatoes together, then he/she is
likely to also buy hamburger.”
• From the standpoint
Dr.Ranjitha M ; Kristu Jayanti College
11. • From the standpoint of customer behaviour,
this defines an association between the set of
products {onion, potato} and the set {burger}.
• This association is represented in the form of
a rule as follows:
{onion, potato} {burger}
Dr.Ranjitha M ; Kristu Jayanti College
12. • The measure of how likely a customer, who
has bought onion and potato, to buy burger
also
is given by the conditional probability
P({onion, potato}|{burger}):
• If this conditional probability is 0.8, then the
rule may be stated more precisely as follows:
• “80% of customers who buy onion and potato
also buy burger.”
Dr.Ranjitha M ; Kristu Jayanti College
13. How association rules are made use of
• Consider an association rule of the form
X Y;
• that is, if people buy X then they are also likely to
buy Y .
• Suppose there is a customer who buys X and
does not buy Y .
• Then that customer is a potential Y customer.
Once we find such customers, we can target them
for cross-selling. A knowledge of such rules can
be used for promotional pricing or product
placements.
Dr.Ranjitha M ; Kristu Jayanti College
14. Classification
2. Definition
• In machine learning, classification is the
problem of identifying to which of a set of
categories a new observation belongs, on the
basis of a training set of data containing
observations (or instances) whose category
membership is known.
Dr.Ranjitha M ; Kristu Jayanti College
15. Real life examples
i) Optical character recognition
Optical character recognition problem, which is the problem of recognizing character codes
from their images, is an example of classification problem.
This is an example where there are multiple classes, as many as there are characters we would
like to recognize. Especially interesting is the case when the characters are handwritten.
People have different handwriting styles; characters may be written small or large, slanted,
with a pen or pencil, and there are many possible images corresponding to the same
character.
Dr.Ranjitha M ; Kristu Jayanti College
16. ii) Face recognition
In the case of face recognition, the input is an
image, the classes are people to be recognized,
and the learning program should learn to associate
the face images to identities. This problem
is more difficult than optical character recognition
because there are more classes, input
image is larger, and a face is three-dimensional and
differences in pose and lighting cause
significant changes in the image.
Dr.Ranjitha M ; Kristu Jayanti College
17. Medical diagnosis
• In medical diagnosis, the inputs are the
relevant information we have about the
patient and
• the classes are the illnesses. The inputs
contain the patient’s age, gender, past medical
• history, and current symptoms. Some tests
may not have been applied to the patient, and
• thus these inputs would be missing.
Dr.Ranjitha M ; Kristu Jayanti College
18. Algorithms
There are several machine learning algorithms for
classification. The following are some of the
• well-known algorithms.
• a) Logistic regression
• b) Naive Bayes algorithm
• c) k-NN algorithm
• d) Decision tree algorithm
• e) Support vector machine algorithm
• f) Random forest algorithm
Dr.Ranjitha M ; Kristu Jayanti College
19. Remarks
• A classification problem requires that examples be
classified into one of two or more classes.
• A classification can have real-valued or discrete input
variables.
• A problem with two classes is often called a two-class or
binary classification problem.
• A problem with more than two classes is often called a
multi-class classification problem.
• A problem where an example is assigned multiple
classes is called a multi-label classification
problem.
Dr.Ranjitha M ; Kristu Jayanti College