Data Mining Classification: Alternative Techniques Lecture Notes for Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar.

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 4/18/2004 1

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2 Rule-Based Classifier l Classify records by using a collection of “if…then…” rules l Rule: (Condition)  y –where  Condition is a conjunctions of attributes  y is the class label –LHS: rule antecedent or condition –RHS: rule consequent –Examples of classification rules:  (Blood Type=Warm)  (Lay Eggs=Yes)  Birds  (Taxable Income < 50K)  (Refund=Yes)  Evade=No

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 3 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

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 4 Application of Rule-Based Classifier l 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

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 5 Rule Coverage and Accuracy l Coverage of a rule: –Fraction of records that satisfy the antecedent of a rule l Accuracy of a rule: –Fraction of records that satisfy both the antecedent and consequent of a rule (Status=Single)  No Coverage = 40%, Accuracy = 50%

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 6 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

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 7 Characteristics of Rule-Based Classifier l Mutually exclusive rules –Classifier contains mutually exclusive rules if the rules are independent of each other –Every record is covered by at most one rule l Exhaustive rules –Classifier has exhaustive coverage if it accounts for every possible combination of attribute values –Each record is covered by at least one rule

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 10 Effect of Rule Simplification l Rules are no longer mutually exclusive –A record may trigger more than one rule –Solution?  Ordered rule set  Unordered rule set – use voting schemes l Rules are no longer exhaustive –A record may not trigger any rules –Solution?  Use a default class

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 11 Ordered Rule Set l Rules are rank ordered according to their priority –An ordered rule set is known as a decision list l When a test record is presented to the classifier –It is assigned to the class label of the highest ranked rule it has triggered –If none of the rules fired, it is assigned to the default class 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

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 12 Rule Ordering Schemes l Rule-based ordering –Individual rules are ranked based on their quality l Class-based ordering –Rules that belong to the same class appear together

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 13 Building Classification Rules l Direct Method:  Extract rules directly from data  e.g.: RIPPER, CN2, Holte’s 1R l Indirect Method:  Extract rules from other classification models (e.g. decision trees, neural networks, etc).  e.g: C4.5rules

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 14 Direct Method: Sequential Covering 1. Start from an empty rule 2. Grow a rule using the Learn-One-Rule function 3. Remove training records covered by the rule 4. Repeat Step (2) and (3) until stopping criterion is met

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 19 Instance Elimination l Why do we need to eliminate instances? –Otherwise, the next rule is identical to previous rule l Why do we remove positive instances? –Ensure that the next rule is different l Why do we remove negative instances? –Prevent underestimating accuracy of rule –Compare rules R2 and R3 in the diagram

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 20 Rule Evaluation l Metrics: –Accuracy –Laplace –M-estimate n : Number of instances covered by rule n c : Number of instances covered by rule k : Number of classes p : Prior probability

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 21 Stopping Criterion and Rule Pruning l Stopping criterion –Compute the gain –If gain is not significant, discard the new rule l Rule Pruning –Similar to post-pruning of decision trees –Reduced Error Pruning:  Remove one of the conjuncts in the rule  Compare error rate on validation set before and after pruning  If error improves, prune the conjunct

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 22 Summary of Direct Method l Grow a single rule l Remove Instances from rule l Prune the rule (if necessary) l Add rule to Current Rule Set l Repeat

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 25 C4.5 versus C4.5rules versus RIPPER C4.5rules: (Give Birth=No, Can Fly=Yes)  Birds (Give Birth=No, Live in Water=Yes)  Fishes (Give Birth=Yes)  Mammals (Give Birth=No, Can Fly=No, Live in Water=No)  Reptiles ( )  Amphibians RIPPER: (Live in Water=Yes)  Fishes (Have Legs=No)  Reptiles (Give Birth=No, Can Fly=No, Live In Water=No)  Reptiles (Can Fly=Yes,Give Birth=No)  Birds ()  Mammals

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 27 Advantages of Rule-Based Classifiers l As highly expressive as decision trees l Easy to interpret l Easy to generate l Can classify new instances rapidly l Performance comparable to decision trees

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 29 Instance Based Classifiers l 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

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 30 Nearest Neighbor Classifiers l 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

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 31 Nearest-Neighbor Classifiers l 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 l 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)

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 34 Nearest Neighbor Classification l Compute distance between two points: –Euclidean distance l 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

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 35 Nearest Neighbor Classification… l 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

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 36 Nearest Neighbor Classification… l 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

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 37 Nearest Neighbor Classification… l 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 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 vs d = 1.4142  Solution: Normalize the vectors to unit length

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 38 Nearest neighbor Classification… l 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

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 39 Example: PEBLS Class Marital Status SingleMarriedDivorced Yes201 No241 Distance between nominal attribute values: d(Single,Married) = | 2/4 – 0/4 | + | 2/4 – 4/4 | = 1 d(Single,Divorced) = | 2/4 – 1/2 | + | 2/4 – 1/2 | = 0 d(Married,Divorced) = | 0/4 – 1/2 | + | 4/4 – 1/2 | = 1 d(Refund=Yes,Refund=No) = | 0/3 – 3/7 | + | 3/3 – 4/7 | = 6/7 Class Refund YesNo Yes03 No34

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 40 Example: PEBLS Distance between record X and record Y: where: w X  1 if X makes accurate prediction most of the time w X > 1 if X is not reliable for making predictions

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 42 Example of Bayes Theorem l Given: –A doctor knows that meningitis causes stiff neck 50% of the time –Prior probability of any patient having meningitis is 1/50,000 –Prior probability of any patient having stiff neck is 1/20 l If a patient has stiff neck, what’s the probability he/she has meningitis?

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 43 Bayesian Classifiers l Consider each attribute and class label as random variables l Given a record with attributes (A 1, A 2,…,A n ) –Goal is to predict class C –Specifically, we want to find the value of C that maximizes P(C| A 1, A 2,…,A n ) l Can we estimate P(C| A 1, A 2,…,A n ) directly from data?

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 44 Bayesian Classifiers l Approach: –compute the posterior probability P(C | A 1, A 2, …, A n ) for all values of C using the Bayes theorem –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) l How to estimate P(A 1, A 2, …, A n | C )?

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 45 Naïve Bayes Classifier l Assume independence among attributes A i when class is given: –P(A 1, A 2, …, A n |C) = P(A 1 | C j ) P(A 2 | C j )… P(A n | C j ) –Can estimate P(A i | C j ) for all A i and C j. –New point is classified to C j if P(C j )  P(A i | C j ) is maximal.

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 46 How to Estimate Probabilities from Data? l Class: P(C) = N c /N –e.g., P(No) = 7/10, P(Yes) = 3/10 l For discrete attributes: P(A i | C k ) = |A ik |/ N c –where |A ik | is number of instances having attribute A i and belongs to class C k –Examples: P(Status=Married|No) = 4/7 P(Refund=Yes|Yes)=0 k

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 47 How to Estimate Probabilities from Data? l For continuous attributes: –Discretize the range into bins  one ordinal attribute per bin  violates independence assumption –Two-way split: (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 (e.g., mean and standard deviation)  Once probability distribution is known, can use it to estimate the conditional probability P(A i |c) k

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 48 How to Estimate Probabilities from Data? l Normal distribution: –One for each (A i,c i ) pair l For (Income, Class=No): –If Class=No  sample mean = 110  sample variance = 2975

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 50 Naïve Bayes Classifier l If one of the conditional probability is zero, then the entire expression becomes zero l Probability estimation: c: number of classes p: prior probability m: parameter

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 52 Naïve Bayes (Summary) l Robust to isolated noise points l Handle missing values by ignoring the instance during probability estimate calculations l Robust to irrelevant attributes l Independence assumption may not hold for some attributes –Use other techniques such as Bayesian Belief Networks (BBN)

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 55 Artificial Neural Networks (ANN) l Model is an assembly of inter-connected nodes and weighted links l Output node sums up each of its input value according to the weights of its links l Compare output node against some threshold t Perceptron Model or

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 57 Algorithm for learning ANN l Initialize the weights (w 0, w 1, …, w k ) l 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 w i ’s that minimize the above objective function  e.g., backpropagation algorithm (see lecture notes)

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 65 Support Vector Machines l 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)

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 70 Ensemble Methods l Construct a set of classifiers from the training data l Predict class label of previously unseen records by aggregating predictions made by multiple classifiers

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 72 Why does it work? l 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:

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 74 Bagging l Sampling with replacement l Build classifier on each bootstrap sample l Each sample has probability (1 – 1/n) n of being selected

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 75 Boosting l 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

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 76 Boosting l Records that are wrongly classified will have their weights increased l 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

Data Mining Classification: Alternative Techniques Lecture Notes for Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar.

Similar presentations

Presentation on theme: "Data Mining Classification: Alternative Techniques Lecture Notes for Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar."— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

Data Mining Classification: Alternative Techniques Lecture Notes for Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar.

Similar presentations

Presentation on theme: "Data Mining Classification: Alternative Techniques Lecture Notes for Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar."— Presentation transcript:

Similar presentations

About project

Feedback