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**Data Mining Classification: Alternative Techniques**

Lecture Notes for Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining /18/

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**Alternative Techniques**

Rule-Based Classifier Classify records by using a collection of “if…then…” rules Instance Based Classifiers

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**Rule-based Classifier (Example)**

R1: (Give Birth = no) (Can Fly = yes) Birds R2: (Give Birth = no) (Live in Water = yes) Fishes R3: (Give Birth = yes) (Blood Type = warm) Mammals R4: (Give Birth = no) (Can Fly = no) Reptiles R5: (Live in Water = sometimes) Amphibians

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**Application of Rule-Based Classifier**

A rule r covers an instance x if the attributes of the instance satisfy the condition of the rule R1: (Give Birth = no) (Can Fly = yes) Birds R2: (Give Birth = no) (Live in Water = yes) Fishes R3: (Give Birth = yes) (Blood Type = warm) Mammals R4: (Give Birth = no) (Can Fly = no) Reptiles R5: (Live in Water = sometimes) Amphibians The rule R1 covers a hawk => Bird The rule R3 covers the grizzly bear => Mammal

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**How does Rule-based Classifier Work?**

R1: (Give Birth = no) (Can Fly = yes) Birds R2: (Give Birth = no) (Live in Water = yes) Fishes R3: (Give Birth = yes) (Blood Type = warm) Mammals R4: (Give Birth = no) (Can Fly = no) Reptiles R5: (Live in Water = sometimes) Amphibians A lemur triggers rule R3, so it is classified as a mammal A turtle triggers both R4 and R5 A dogfish shark triggers none of the rules

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**From Decision Trees To Rules**

Rules are mutually exclusive and exhaustive Rule set contains as much information as the tree

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**Rules Can Be Simplified**

Initial Rule: (Refund=No) (Status=Married) No Simplified Rule: (Status=Married) No

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**Instance-Based Classifiers**

Store the training records Use training records to predict the class label of unseen cases

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**Instance Based Classifiers**

Examples: Rote-learner Memorizes entire training data and performs classification only if attributes of record match one of the training examples exactly Nearest neighbor Uses k “closest” points (nearest neighbors) for performing classification

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**Nearest Neighbor Classifiers**

Basic idea: If it walks like a duck, quacks like a duck, then it’s probably a duck Training Records Test Record Compute Distance Choose k of the “nearest” records

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**Nearest-Neighbor Classifiers**

Requires three things The set of stored records Distance Metric to compute distance between records The value of k, the number of nearest neighbors to retrieve To classify an unknown record: Compute distance to other training records Identify k nearest neighbors Use class labels of nearest neighbors to determine the class label of unknown record (e.g., by taking majority vote)

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**Definition of Nearest Neighbor**

K-nearest neighbors of a record x are data points that have the k smallest distance to x

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**Nearest Neighbor Classification**

Compute distance between two points: Euclidean distance Determine the class from nearest neighbor list take the majority vote of class labels among the k-nearest neighbors Weigh the vote according to distance weight factor, w = 1/d2

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**Nearest Neighbor Classification…**

Choosing the value of k: If k is too small, sensitive to noise points If k is too large, neighborhood may include points from other classes

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**Nearest Neighbor Classification…**

Scaling issues Attributes may have to be scaled to prevent distance measures from being dominated by one of the attributes Example: height of a person may vary from 1.5m to 1.8m weight of a person may vary from 90lb to 300lb income of a person may vary from $10K to $1M Solution: Normalize the vectors to unit length

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**Nearest neighbor Classification…**

k-NN classifiers are lazy learners It does not build models explicitly Unlike eager learners such as decision tree induction and rule-based systems Classifying unknown records are relatively expensive

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**Artificial Neural Networks (ANN)**

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**Artificial Neural Networks (ANN)**

What is ANN?

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**Artificial Neural Networks (ANN)**

Axon Nucleus Cell Body Synapse Dendrites Output (Y) w1 w2 w3 b S y x1 x2 x3 Input (X) Weight Neuron

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**Artificial Neural Networks (ANN)**

Output Y is 1 if at least two of the three inputs are equal to 1.

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**Artificial Neural Networks (ANN)**

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**Artificial Neural Networks (ANN)**

Model is an assembly of inter-connected nodes and weighted links Output node sums up each of its input value according to the weights of its links Compare output node against some threshold t Perceptron Model or

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**General Structure of ANN**

Training ANN means learning the weights of the neurons

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**Algorithm for learning ANN**

Initialize the weights (w0, w1, …, wk) Adjust the weights in such a way that the output of ANN is consistent with class labels of training examples Objective function: Find the weights wi’s that minimize the above objective function e.g., backpropagation algorithm (see lecture notes)

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**Support Vector Machines**

Find a linear hyperplane (decision boundary) that will separate the data

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**Support Vector Machines**

One Possible Solution

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**Support Vector Machines**

Another possible solution

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**Support Vector Machines**

Other possible solutions

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**Support Vector Machines**

Which one is better? B1 or B2? How do you define better?

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**Support Vector Machines**

Find hyperplane maximizes the margin => B1 is better than B2

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**Support Vector Machines**

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**Support Vector Machines**

We want to maximize: Which is equivalent to minimizing: But subjected to the following constraints: This is a constrained optimization problem Numerical approaches to solve it (e.g., quadratic programming)

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**Support Vector Machines**

What if the problem is not linearly separable?

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**Support Vector Machines**

What if the problem is not linearly separable? Introduce slack variables Need to minimize: Subject to:

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**Nonlinear Support Vector Machines**

What if decision boundary is not linear?

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**Nonlinear Support Vector Machines**

Transform data into higher dimensional space

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**Ensemble Methods Construct a set of classifiers from the training data**

Predict class label of previously unseen records by aggregating predictions made by multiple classifiers

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General Idea

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**Why does it work? Suppose there are 25 base classifiers**

Each classifier has error rate, = 0.35 Assume classifiers are independent Probability that the ensemble classifier makes a wrong prediction:

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**Examples of Ensemble Methods**

How to generate an ensemble of classifiers? Bagging Boosting

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**Bagging Sampling with replacement**

Build classifier on each bootstrap sample Each sample has probability (1 – 1/n)n of being selected

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Boosting An iterative procedure to adaptively change distribution of training data by focusing more on previously misclassified records Initially, all N records are assigned equal weights Unlike bagging, weights may change at the end of boosting round

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Boosting Records that are wrongly classified will have their weights increased Records that are classified correctly will have their weights decreased Example 4 is hard to classify Its weight is increased, therefore it is more likely to be chosen again in subsequent rounds

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**Example: AdaBoost Base classifiers: C1, C2, …, CT Error rate:**

Importance of a classifier:

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**Example: AdaBoost Weight update:**

If any intermediate rounds produce error rate higher than 50%, the weights are reverted back to 1/n and the resampling procedure is repeated Classification:

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**Illustrating AdaBoost**

Initial weights for each data point Data points for training

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**Illustrating AdaBoost**

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