DATA MINING LECTURE 10B. Classification k-nearest neighbor classifier Naïve Bayes Logistic Regression Support Vector Machines

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DATA MINING LECTURE 10B Classification k-nearest neighbor classifier Naïve Bayes Logistic Regression Support Vector Machines

NEAREST NEIGHBOR CLASSIFICATION

10 10 Illustrating Classification Task Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No 10 No Small 90K Yes Training Set Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K? 12 Yes Medium 80K? 13 Yes Large 110K? 14 No Small 95K? 15 No Large 67K? Test Set Induction Deduction Learning algorithm Learn Model Apply Model Model

Instance-Based Classifiers Set of Stored Cases Atr1... AtrN Class A B B C A C B Store the training records Use training records to predict the class label of unseen cases Unseen Case Atr1... AtrN

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 classifier Uses k closest points (nearest neighbors) for performing classification

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

Nearest-Neighbor Classifiers Unknown record 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: 1. Compute distance to other training records 2. Identify k nearest neighbors 3. Use class labels of nearest neighbors to determine the class label of unknown record (e.g., by taking majority vote)

Definition of Nearest Neighbor X X X (a) 1-nearest neighbor (b) 2-nearest neighbor (c) 3-nearest neighbor K-nearest neighbors of a record x are data points that have the k smallest distance to x

1 nearest-neighbor Voronoi Diagram defines the classification boundary The area takes the class of the green point

Nearest Neighbor Classification Compute distance between two points: Euclidean distance d( p, q) i ( p q i i ) 2 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/d 2

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 X

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

Nearest Neighbor Classification Problem with Euclidean measure: High dimensional data curse of dimensionality Can produce counter-intuitive results 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 vs 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 d = 1.4142 d = 1.4142 Solution: Normalize the vectors to unit length

Nearest neighbor Classification k-nn classifiers are lazy learners It does not build models explicitly Unlike eager learners such as decision trees Classifying unknown records are relatively expensive Naïve algorithm: O(n) Need for structures to retrieve nearest neighbors fast. The Nearest Neighbor Search problem.

Nearest Neighbor Search Two-dimensional kd-trees A data structure for answering nearest neighbor queries in R 2 kd-tree construction algorithm Select the x or y dimension (alternating between the two) Partition the space into two with a line passing from the median point Repeat recursively in the two partitions as long as there are enough points

Nearest Neighbor Search 2-dimensional kd-trees

Nearest Neighbor Search 2-dimensional kd-trees

Nearest Neighbor Search 2-dimensional kd-trees

Nearest Neighbor Search 2-dimensional kd-trees

Nearest Neighbor Search 2-dimensional kd-trees

Nearest Neighbor Search 2-dimensional kd-trees

Nearest Neighbor Search 2-dimensional kd-trees region(u) all the black points in the subtree of u

Nearest Neighbor Search 2-dimensional kd-trees A binary tree: Size O(n) Depth O(logn) Construction time O(nlogn) Query time: worst case O(n), but for many cases O(logn) Generalizes to d dimensions Example of Binary Space Partitioning

SUPPORT VECTOR MACHINES

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

Support Vector Machines B 1 One Possible Solution

Support Vector Machines B 2 Another possible solution

Support Vector Machines B 2 Other possible solutions

Support Vector Machines B 1 B 2 Which one is better? B1 or B2? How do you define better?

Support Vector Machines B 1 B 2 b 21 b 22 margin b 11 Find hyperplane maximizes the margin => B1 is better than B2 b 12

Support Vector Machines B 1 w x b 0 w x w x b 1 b 1 b 11 f 1 if w x b 1 ( x) 1 if w x b 1 b 12 Margin 2 w

Support Vector Machines We want to maximize: Margin Which is equivalent to minimizing: 2 2 w But subjected to the following constraints: L( w) w x i + b 1 if y i = 1 w x i + b 1 if y i = 1 w 2 2 This is a constrained optimization problem Numerical approaches to solve it (e.g., quadratic programming)

Support Vector Machines What if the problem is not linearly separable?

Support Vector Machines What if the problem is not linearly separable? ξ i w

Support Vector Machines What if the problem is not linearly separable? Introduce slack variables Need to minimize: Subject to: 2 N w k L( w) C i 2 i 1 w x i + b 1 ξ i if y i = 1 w x i + b 1 + ξ i if y i = 1

Nonlinear Support Vector Machines What if decision boundary is not linear?

Nonlinear Support Vector Machines Transform data into higher dimensional space Use the Kernel Trick

LOGISTIC REGRESSION

Classification via regression Instead of predicting the class of an record we want to predict the probability of the class given the record The problem of predicting continuous values is called regression problem General approach: find a continuous function that models the continuous points.

Example: Linear regression Given a dataset of the form (x 1, y 1 ),, (x n, y n ) find a linear function that given the vector x i predicts the y i value as y i = w T x i Find a vector of weights w that minimizes the sum of square errors y i y i 2 i Several techniques for solving the problem.

Classification via regression Assume a linear classification boundary For the positive class the bigger the value of w x, the further the point is from the classification boundary, the higher our certainty for the membership to the positive class Define P(C + x) as an increasing function of w x For the negative class the smaller the value of w x, the further the point is from the classification boundary, the higher our certainty for the membership to the negative class Define P(C x) as a decreasing function of w x w x = 0 w x < 0 w x > 0

Logistic Regression The logistic function f t = P C + x = P C x = 1 1 + e t 1 1 + e w x e w x 1 + e w x log P C + x P C x = w x Logistic Regression: Find the vector w that maximizes the probability of the observed data Linear regression on the log-odds ratio

Logistic Regression Produces a probability estimate for the class membership which is often very useful. The weights can be useful for understanding the feature importance. Works for relatively large datasets Fast to apply.

NAÏVE BAYES CLASSIFIER

Bayes Classifier A probabilistic framework for solving classification problems A, C random variables Joint probability: Pr(A=a,C=c) Conditional probability: Pr(C=c A=a) Relationship between joint and conditional probability distributions Pr( C, A) Pr( C A) Pr( A) Pr( A C) Pr( C) Bayes Theorem: P( C A) P( A C) P( C) P( A)

10 Bayesian Classifiers Consider each attribute and class label as random variables categorica l Tid Refund Marital Status categorica l Taxable Income continuous 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No Evade 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No class Evade C Event space: {Yes, No} P(C) = (0.3, 0.7) Refund A 1 Event space: {Yes, No} P(A 1 ) = (0.3,0.7) Martial Status A 2 Event space: {Single, Married, Divorced} P(A 2 ) = (0.4,0.4,0.2) 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes Taxable Income A 3 Event space: R P(A 3 ) ~ Normal(, )

Bayesian Classifiers Given a record X over attributes (A 1, A 2,,A n ) E.g., X = ( Yes, Single, 125K) The goal is to predict class C Specifically, we want to find the value c of C that maximizes P(C=c X) Maximum Aposteriori Probability estimate Can we estimate P(C X) directly from data? This means that we estimate the probability for all possible values of the class variable.

Bayesian Classifiers Approach: compute the posterior probability P(C A 1, A 2,, A n ) for all values of C using the Bayes theorem P( C A A 1 2 A n ) P( A A A C) P( C) 1 2 n P( A A A ) 1 2 n Choose value of C that maximizes P(C A 1, A 2,, A n ) Equivalent to choosing value of C that maximizes P(A 1, A 2,, A n C) P(C) How to estimate P(A 1, A 2,, A n C )?

Naïve Bayes Classifier Assume independence among attributes A i when class is given: P(A 1, A 2,, A n C) = P(A 1 C) P(A 2 C P(A n C) We can estimate P(A i C) for all values of A i and C. New point X is classified to class c if P C = c X = P C = c P(A i c) i is maximum over all possible values of C.

10 How to Estimate Probabilities from Data? categorica l Tid Refund Marital Status categorica l Taxable Income continuous 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No Evade 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes class Class Prior Probability: P C = c = N c N e.g., P(C = No) = 7/10, P(C = Yes) = 3/10 For discrete attributes: P A i = a C = c = N a,c N c where N a,c is number of instances having attribute A i = a and belongs to class c Examples: P(Status=Married No) = 4/7 P(Refund=Yes Yes)=0

How to Estimate Probabilities from Data? For continuous attributes: Discretize the range into bins one ordinal attribute per bin violates independence assumption Two-way split: (A < v) or (A > v) choose only one of the two splits as new attribute Probability density estimation: Assume attribute follows a normal distribution Use data to estimate parameters of distribution (i.e., mean and standard deviation ) Once probability distribution is known, we can use it to estimate the conditional probability P(A i c)

10 How to Estimate Probabilities from Data? categorica l Tid Refund Marital Status categorica l Taxable Income continuous 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No Evade 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes class Normal distribution: P( A a c One for each (a i,c i ) pair For (Income, Class=No): If Class=No i ) 2 sample mean = 110 ( a 2 sample variance = 2975 j 1 2 ij e 2 ij ) 2 ij P( Income 120 No) 1 2 (54.54) e (120 110 ) 2 ( 2975 ) 2 0.0072

Example of Naïve Bayes Classifier Creating a Naïve Bayes Classifier, essentially means to compute counts: Total number of records: N = 10 Class No: Number of records: 7 Attribute Refund: Yes: 3 No: 4 Attribute Marital Status: Single: 2 Divorced: 1 Married: 4 Attribute Income: mean: 110 variance: 2975 Class Yes: Number of records: 3 Attribute Refund: Yes: 0 No: 3 Attribute Marital Status: Single: 2 Divorced: 1 Married: 0 Attribute Income: mean: 90 variance: 25

Example of Naïve Bayes Classifier Given a Test Record: X naive Bayes Classifier: ( Refund No, Married,Income 120K) P(Refund=Yes No) = 3/7 P(Refund=No No) = 4/7 P(Refund=Yes Yes) = 0 P(Refund=No Yes) = 1 P(Marital Status=Single No) = 2/7 P(Marital Status=Divorced No)=1/7 P(Marital Status=Married No) = 4/7 P(Marital Status=Single Yes) = 2/7 P(Marital Status=Divorced Yes)=1/7 P(Marital Status=Married Yes) = 0 For taxable income: If class=no: sample mean=110 sample variance=2975 If class=yes: sample mean=90 sample variance=25 P(X Class=No) = P(Refund=No Class=No) P(Married Class=No) P(Income=120K Class=No) = 4/7 4/7 0.0072 = 0.0024 P(X Class=Yes) = P(Refund=No Class=Yes) P(Married Class=Yes) P(Income=120K Class=Yes) = 1 0 1.2 10-9 = 0 P(No) = 0.3, P(Yes) = 0.7 Since P(X No)P(No) > P(X Yes)P(Yes) Therefore P(No X) > P(Yes X) => Class = No

Naïve Bayes Classifier If one of the conditional probability is zero, then the entire expression becomes zero Probability estimation: Original : P( A Laplace: P( A i i a C m - estimate : P( A a C i c) c) a C N N N N c ac c 1 N ac c) N N i ac c mp m N i : number of attribute values for attribute A i p: prior probability m: parameter

Example of Naïve Bayes Classifier Given a Test Record: X naive Bayes Classifier: ( Refund No, Married,Income 120K) With Laplace Smoothing P(Refund=Yes No) = 4/9 P(Refund=No No) = 5/9 P(Refund=Yes Yes) = 1/5 P(Refund=No Yes) = 4/5 P(Marital Status=Single No) = 3/10 P(Marital Status=Divorced No)=2/10 P(Marital Status=Married No) = 5/10 P(Marital Status=Single Yes) = 3/6 P(Marital Status=Divorced Yes)=2/6 P(Marital Status=Married Yes) = 1/6 For taxable income: If class=no: sample mean=110 sample variance=2975 If class=yes: sample mean=90 sample variance=25 P(X Class=No) = P(Refund=No Class=No) P(Married Class=No) P(Income=120K Class=No) = 5/9 5/10 0.0072 P(X Class=Yes) = P(Refund=No Class=Yes) P(Married Class=Yes) P(Income=120K Class=Yes) = 4/5 1/6 1.2 10-9 P(No) = 0.7, P(Yes) = 0.3 Since P(X No)P(No) > P(X Yes)P(Yes) Therefore P(No X) > P(Yes X) => Class = No

Implementation details Computing the conditional probabilities involves multiplication of many very small numbers Numbers get very close to zero, and there is a danger of numeric instability We can deal with this by computing the logarithm of the conditional probability log P C A ~ log P A C + log P A = log A i C + log P(A) i

Naïve Bayes for Text Classification Naïve Bayes is commonly used for text classification For a document d = (t 1,, t k ) P c d = P(c) P(t i c) t i d P t i c = Fraction of terms from all documents in c that are t i. Easy to implement and works relatively well Limitation: Hard to incorporate additional features (beyond words).

Naïve Bayes (Summary) Robust to isolated noise points Handle missing values by ignoring the instance during probability estimate calculations Robust to irrelevant attributes Independence assumption may not hold for some attributes Use other techniques such as Bayesian Belief Networks (BBN) Naïve Bayes can produce a probability estimate, but it is usually a very biased one Logistic Regression is better for obtaining probabilities.

Generative vs Discriminative models Naïve Bayes is a type of a generative model Generative process: First pick the category of the record Then given the category, generate the attribute values from the distribution of the category Conditional independence given C C A 1 A 2 A n We use the training data to learn the distribution of the values in a class

Generative vs Discriminative models Logistic Regression and SVM are discriminative models The goal is to find the boundary that discriminates between the two classes from the training data In order to classify the language of a document, you can Either learn the two languages and find which is more likely to have generated the words you see Or learn what differentiates the two languages.

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