This week our topic shifts to the classification concepts in chapter four. Therefore, answer the following questions: What are the various types of classifiers? What is a rule-based classifier? What is the difference between nearest neighbor and naïve bayes classifiers? What is logistic regression? -Page-1 -format-apa -reference-textbook mandatory
homework/week-5/IT632_Chapter 4 PPT – Beeline.html
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© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 1
Classification: Alternative Techniques
Lecture Notes for Chapter 4
Introduction to Data Mining
by
Tan, Steinbach, Kumar
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 2
Types of Classifiers
Binary vs. Multiclass
Deterministic vs. Probablistic
Linear vs. Nonlinear
Global vs. local
Generative vs. Discriminative
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 3
Rule Based Classifiers
How it works?
Properties of a Rule Set
Direct Methods for Rule Extraction
Learn-One rule function
Instance Elimination
Indirect Methods for Rule Extraction
Characteristics of Rule-Based Classifiers
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 4
Nearest Neighbor Classifiers
Algorithm
Computes the distance or similarity between each test instance and all training examples.
Characteristics Review 4.3.2
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 5
ve Bayes Classifier
Basics of Probability Theory
Bayes Theorem
Bayes theorem presents the statistical principle for answering questions like the previous one, where evidence from multiple sources has to be combined with prior beliefs to arrive at predictions. Bayes theorem can be briefly described as follows.
Classification
Class conditional
Generative classification
Prior probabilty
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 6
Bayesian Network
Graphical Representation
Conditional Independence
Joint Probability
Use of Hidden Variables
Inference and Learning
Variable Elimination
Sum-Product Algorithm for Trees
Generalizations for Non-Tree Graphs
Learning Model Parameters
Characteristics of Bayesian Networks
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 7
Logistic Regression
Generalized Linear Model
Learning Model Parameters
Characteristics
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 8
Artificial Neural Network (ANN)
Perceptron
Learning the Perceptron
Multi-layer Neural Network
Learning Model Parameters
Characteristics of ANN
Universal approximators
Review 4.7.3
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 9
Deep Learning
Using Synergistic Loss Functions
Saturation of outputs and Cross entropy loss function
Using Responsive Activation Functions
Vanishing gradient problem and ReLU
Regularization
Dropout
Initialization of Model Parameters
Supervised and unsupervised pretraining
Use of autoencoders and hybrid pretraining
Characteristics of Deep Learning
Review 4.8.5
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 10
Support Vector Machine (SVM)
Margin of a Separating Hyperplane
Rationale for maximum margin
Linear SVM
Learning model parameters
Soft-margin SVM
Regularizer of Hinge Loss
Nonlinear SVM
Attribute transformation
Learning a non-linear SVM Model
Characteristics of SVM
Review Section 4.9.5
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 11
Ensemble Methods
Rationale for Ensemble Methods
Methods for Constructing an Ensemble Classifier
Bias- Variance Decomposition
Bagging
Boosting
AdaBoost
Random Forests
Empirical Comparison among Ensemble methods
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 12
Class Imbalance Problem
Building Classifiers with Class Imbalance
Oversampling and undersampling
Assigning scores to test instances
Evaluating Performance with Class Imbalance
Finding an Optimal Score Threshold
Aggregate Evaluation of Performance
ROC Curve
Precision-Recall Curve
© Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 13
Multiclass Problem
multiclass problem is one where the data is divided into more than two categories.
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homework/week-5/Week 5 Discussion – Beeline.html
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This week our topic shifts to the classification concepts in chapter four. Therefore, answer the following questions:
What are the various types of classifiers?
What is a rule-based classifier?
What is the difference between nearest neighbor and naïve bayes classifiers?
What is logistic regression?
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homework/week-5/Week 5 Overview and Objectives – Beeline.html
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Overview and Objectives
This week we focus on the various types of classifiers and understanding the key components to logistic regression.
Objectives:
Define the various types of classifiers.
Understand the key components to logic regression.
Compare and contrast nearest neighbor and naïve Bayes classifiers.
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homework/week-6/Week 6 Learning Materials – Beeline.html
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Learning Materials
Read:
Hemmatian, H. (2019). A survey on classification techniques for opinion mining and sentiment analysis. Artificial Intelligence Review, 52(3), 1495â1545.
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homework/week-6/Week 6 Overview and Objectives – Beeline.html
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Overview and Objectives
In week six, we turn our focus to a real world example on opinion mining and gain a better understanding of NLP framework.
Objectives:
Discuss a real-world example on opinion mining and how it is used in information retrieval.
Explain the various components and techniques of opinion mining and the importance to transforming an organizations NLP framework.
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homework/week-6/week-6-homework.txt
Review the article by Hemmatian (2019), on classification techniques. In essay format answer the following questions: What were the results of the study? Note what opinion mining is and how its used in information retrieval. Discuss the various concepts and techniques of opinion mining and the importance to transforming an organizations NLP framework. In an APA7 formatted essay answer all questions above. There should be headings to each of the questions above as well. Ensure there are at least two-peer reviewed sources to support your work. The paper should be at least two pages of content (this does not include the cover page or reference page). -Pages-2 -Format-APA -References-Mandatory text book
