Discussion – 400 words. APA format – 8 hours

 DATA MINING – 

 Therefore, answer the following questions: 

1. What are the various types of classifiers?
2. What is a rule-based classifier?
3. What is the difference between nearest neighbor and naïve bayes classifiers?
4. What is logistic regression?

Research 

watch

https://www.youtube.com/watch?v=yFKVI7vgPPs

 Read:

1. ch. 4 in textbook: Classification: Alternative Techniques

Classification: Alternative Techniques
Lecture Notes for Chapter 4
Introduction to Data Mining
by
Tan, Steinbach, Kumar

Types of Classifiers
Binary vs. Multiclass
Deterministic vs. Probablistic
Linear vs. Nonlinear
Global vs. local
Generative vs. Discriminative

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

Nearest Neighbor Classifiers
Algorithm
Computes the distance or similarity between each test instance and all training examples.
Characteristics – Review 4.3.2

Naïve 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

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

Logistic Regression
Generalized Linear Model
Learning Model Parameters
Characteristics

Artificial Neural Network (ANN)
Perceptron
Learning the Perceptron
Multi-layer Neural Network
Learning Model Parameters
Characteristics of ANN
Universal approximators
Review 4.7.3

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

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

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

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

Multiclass Problem
A multiclass problem is one where the data is divided into more than two categories.