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Komate AMPHAWAN CLASSIFICATION: BASIC CONCEPT 1 Outline • Classification: Basic Concepts • Decision Tree Induction • Bayes Classification Methods • Rule-Based Classification • Model Evaluation and Selection • Techniques to Improve Classification Accuracy: Ensemble Methods • Summary 2 WHAT IS CLASSIFICATION? 3 What is Classification? • The goal of data classification is to organize and categorize data in distinct classes. A model is first created based on the data distribution. The model is then used to classify new data. Given the model, a class can be predicted for new data. 4 Examples of Classification Task • Classifying credit card transactions as legitimate (ถูกต้ องตามเหตุผล) or fraudulent (ซึงฉ้ อโกง) • Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil • Categorizing news stories as finance, weather, entertainment, sports, etc 5 Classification vs. Prediction (1) • Classification predicts categorical class labels (discrete or nominal) classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data • Prediction models continuous-valued functions, i.e., predicts unknown or missing values 6 Classification vs. Prediction (2) • Typical applications Credit approval Target marketing Medical diagnosis Fraud detection Web page categorization 7 CLASSIFICATION = LEARNING A MODEL 8 Classification = Learning a Model 9 Supervised vs. Unsupervised Learning • Supervised learning (classification) Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations New data is classified based on the training set • Unsupervised learning (clustering) The class labels of training data is unknown Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data 10 Classification is a three-step process 1. Model construction (Learning): Each tuple is assumed to belong to a predefined class, as determined by one of the attributes, called the class label. The set of all tuples used for construction of the model is called training set. The model is represented in the following forms: 11 1. Classification Process (Learning) 12 Classification is a three-step process 2. Model Evaluation (Accuracy): • Estimate accuracy rate of the model based on a test set. The known label of test sample is compared with the classified result from the model. Accuracy rate is the percentage of test set samples that are correctly classified by the model. Test set is independent of training set otherwise over-fitting will occur. 13 2. Classification Process (Accuracy Evaluation) 14 Classification is a three-step process 3. Model Use (Classification): • The model is used to classify unseen objects. Give a class label to a new tuple Predict the value of an actual attribute 15 3. Classification Process (Classification) 16 Framework (Supervised Learning) 17 ISSUES REGARDING CLASSIFICATION AND PREDICTION 18 Issues: Data Preparation • Data cleaning Preprocess data in order to reduce noise and handle missing values • Relevance analysis (feature selection) Remove the irrelevant or redundant attributes • Data transformation Generalize and/or normalize data 19 Issues: Evaluating Classification Methods (1) • Accuracy classifier accuracy: predicting class label predictor accuracy: guessing value of predicted attributes • Speed time to construct the model (training time) time to use the model (classification/prediction time) 20 Issues: Evaluating Classification Methods (2) • Robustness handling noise and missing values • Scalability efficiency in disk-resident databases • Interpretability understanding and insight provided by the model • Other measures e.g., goodness of rules, such as decision tree size or compactness of classification rules 21 CLASSIFICATION METHODS 22 • • • • • • • • • • Decision Tree Induction Neural Networks Bayesian Classification Associative Classifiers K-Nearest Neighbour Support Vector Machines Case-Based Reasoning Genetic Algorithms Rough Set Theory Fuzzy Sets 23 DECISION TREE 24 What is a Decision Tree? • A decision tree is a flow-chart-like tree structure. • Internal node denotes a test on an attribute • Branch represents an outcome of the test All tuples in branch have the same value for the tested attribute. • Leaf node represents class label or class label distribution. 25 Training Dataset 26 A Sample Decision Tree 27 Decision-Tree Classification Methods • The basic top-down decision tree generation approach usually consists of two phases: 1. Tree construction At the start, all the training examples are at the root. Partition examples are recursively based on selected attributes. 2. Tree pruning Aiming at removing tree branches that may reflect noise in the training data and lead to errors when classifying test data improve classification accuracy. 28 Decision Tree Construction Recursive process: • Tree starts a single node representing all data. • If sample are all same class then node becomes a leaf labeled with class label. • Otherwise, select attribute that best separates sample into individual classes • Recursion stops when: Sample in node belong to the same class (majority); There are no remaining attributes on which to split; There are no samples with attribute value. 29 Pseudo code of decision tree generation (1) 30 Pseudo code of decision tree generation (2) 31 Example of decision tree 32 Choosing the Attribute to Split Data Set • The measure is also called Goodness function • Different algorithms may use different goodness functions: information gain (ID3/C4.5) • assume all attributes to be categorical. • can be modified for continuous-valued attributes. gini index • assume all attributes are continuous-valued. • assume there exist several possible split values for each attribute. • may need other tools, such as clustering, to get the possible split values. 33 • can be modified for categorical attributes. Information Gain (1) • Select the attribute with the highest information gain • Let pi be the probability that an arbitrary tuple in D belongs to class Ci, estimated by |Ci, D|/|D| • Expected information (entropy) needed to classify a tuple in D: m Info ( D ) = − ∑ pi log 2 ( pi ) i =1 34 Information Gain (1) • Information needed (after using A to split D into v partitions) to classify D: Info A ( D ) = v | Dj | j =1 |D| ∑ × Info ( D j ) • Information gained by branching on attribute A Gain(A) = Info(D) − Info A(D) 35 Attribute Selection: Information Gain g g Class P: buys_computer = “yes” Class N: buys_computer = “no” Info ( D ) = I (9,5 ) = − age <=30 31…40 >40 age <=30 <=30 31…40 >40 >40 >40 31…40 <=30 <=30 >40 <=30 31…40 31…40 36 >40 Info age ( D ) = 5 + I (3, 2 ) = 0 .694 14 9 9 5 5 log 2 ( ) − log 2 ( ) = 0 .940 14 14 14 14 pi 2 4 3 n i I(p i, n i) 3 0.971 0 0 2 0.971 income student credit_rating high no fair high no excellent high no fair medium no fair low yes fair low yes excellent low yes excellent medium no fair low yes fair medium yes fair medium yes excellent medium no excellent high yes fair medium no excellent 5 4 I ( 2 ,3 ) + I ( 4,0 ) 14 14 5 I ( 2,3) means “age <=30” has 5 out of 14 14 samples, with 2 yes’es and 3 no’s. Hence buys_computer no no yes yes yes no yes no yes yes yes yes yes no Gain ( age ) = Info ( D ) − Info age ( D ) = 0 .246 Similarly, Gain(income) = 0.029 Gain( student ) = 0.151 Gain(credit _ rating ) = 0.048 37 38 39 40 More example in decision tree on playing tennis ความชุ่มชื $น มีเมฆมาก 41 42 43 44 45 Gain Ratio for Attribute Selection • Information gain measure is biased towards attributes with a large number of values • C4.5 (a successor of ID3) uses gain ratio to overcome the problem (normalization to information gain) v SplitInfo A ( D) = −∑ j =1 | Dj | |D| × log 2 ( | Dj | |D| ) • GainRatio(A) = Gain(A)/SplitInfo(A) • Note that Gain(A) is the information gain of attribute A 46 age <=30 <=30 31…40 >40 >40 >40 31…40 <=30 <=30 >40 <=30 31…40 31…40 >40 income student credit_rating high no fair high no excellent high no fair medium no fair low yes fair low yes excellent low yes excellent medium no fair low yes fair medium yes fair medium yes excellent medium no excellent high yes fair medium no excellent buys_computer no no yes yes yes no yes no yes yes yes yes yes no Example gain_ratio(income) = 0.029/1.557 = 0.019 • The attribute with the maximum gain ratio is selected as the splitting attribute 47 48 49 50 51 Gini Index (CART, IBM IntelligentMiner) • If a data set D contains examples from n classes, gini index, gini(D) is defined as n gini ( D ) = 1 − ∑ p 2j j =1 where pj is the relative frequency of class j in D • If a data set D is split on A into two subsets D1 and D2, the gini index gini(D) is defined as | D1 | |D2 | gini ( D 1) + gini ( D 2 ) gini A ( D ) = |D | |D | • Reduction in Impurity: ∆gini( A) = gini( D) − giniA ( D) 52 Gini Index • The attribute provides the smallest ginisplit(D) (or the largest reduction in impurity) is chosen to split the node (need to enumerate all the possible splitting points for each attribute) 53 Computation of Gini Index age <=30 <=30 31…40 >40 >40 >40 31…40 <=30 <=30 >40 <=30 31…40 31…40 >40 income student credit_rating high no fair high no excellent high no fair medium no fair low yes fair low yes excellent low yes excellent medium no fair low yes fair medium yes fair medium yes excellent medium no excellent high yes fair medium no excellent buys_computer no no yes yes yes no yes no yes yes yes yes yes no • D has 9 tuples in buys_computer = “yes” and 5 in “no” 2 2 9 5 gini( D) = 1 − − = 0.459 14 14 54 age <=30 <=30 31…40 >40 >40 >40 31…40 <=30 <=30 >40 <=30 31…40 31…40 >40 Computation of Gini Index income student credit_rating high no fair high no excellent high no fair medium no fair low yes fair low yes excellent low yes excellent medium no fair low yes fair medium yes fair medium yes excellent medium no excellent high yes fair medium no excellent buys_computer no no yes yes yes no yes no yes yes yes yes yes no • Suppose the attribute income partitions D into 10 in D1: {low, medium} and 4 in D2 10 4 giniincome∈{low,medium} ( D) = Gini( D1 ) + Gini( D1 ) 14 14 • Gini{low,high} is 0.458; Gini{medium,high} is 0.450. Thus, split on the {low,medium} (and {high}) since it has the lowest Gini index 55 Computation of Gini Index • All attributes are assumed continuous-valued • May need other tools, e.g., clustering, to get the possible split values • Can be modified for categorical attributes 56 57 58 59 60 Comparing Attribute Selection Measures The three measures, in general, return good results but • Information gain: • biased towards multivalued attributes • Gain ratio: tends to prefer unbalanced splits in which one partition is much smaller than the others • Gini index: biased to multivalued attributes has difficulty when # of classes is large tends to favor tests that result in equal-sized partitions and purity in both partitions 61 Other Attribute Selection Measures (1) • CHAID: a popular decision tree algorithm, measure based on χ2 test for independence • C-SEP: performs better than info. gain and gini index in certain cases • G-statistics: has a close approximation to χ2 distribution • MDL (Minimal Description Length) principle (i.e., the simplest solution is preferred): The best tree as the one that requires the fewest # of bits to both (1) encode the tree, and (2) encode the exceptions to the tree 62 Other Attribute Selection Measures (1) • Multivariate splits (partition based on multiple variable combinations) CART: finds multivariate splits based on a linear comb. of attrs. • Which attribute selection measure is the best? Most give good results, none is significantly superior than others 63 • Underfitting and Overfitting • Missing Values • Costs of Classification PRACTICAL ISSUES OF CLASSIFICATION 64 Underfitting and Overfitting (Example) 65 Overfitting due to Noise 66 Problem of decision tree • Overfitting: An induced tree may overfit the training data Too many branches, some may reflect anomalies due to noise or outliers Poor accuracy for unseen samples 67 Overfitting and Tree Pruning • Two approaches to avoid overfitting Prepruning: Halt tree construction early—do not split a node if this would result in the goodness measure falling below a threshold • Difficult to choose an appropriate threshold Postpruning: Remove branches from a “fully grown” tree—get a sequence of progressively pruned trees • Use a set of data different from the training data to decide which is the “best pruned tree” 68 Enhancements to Basic Decision Tree Induction (1) • Allow for continuous-valued attributes Dynamically define new discrete-valued attributes that partition the continuous attribute value into a discrete set of intervals • Handle missing attribute values Assign the most common value of the attribute Assign probability to each of the possible values 69 Enhancements to Basic Decision Tree Induction (2) • Attribute construction Create new attributes based on existing ones that are sparsely represented This reduces fragmentation, repetition, and replication 70 Classification in Large Databases (1) • Classification—a classical problem extensively studied by statisticians and machine learning researchers • Scalability: Classifying data sets with millions of examples and hundreds of attributes with reasonable speed 71 Classification in Large Databases (2) • Why decision tree induction in data mining? relatively faster learning speed (than other classification methods) convertible to simple and easy to understand classification rules can use SQL queries for accessing databases comparable classification accuracy with other methods 72 Scalable Decision Tree Induction Methods • SLIQ (EDBT’96 — Mehta et al.) Builds an index for each attribute and only class list and the current attribute list reside in memory • SPRINT (VLDB’96 — J. Shafer et al.) Constructs an attribute list data structure • PUBLIC (VLDB’98 — Rastogi & Shim) Integrates tree splitting and tree pruning: stop growing the tree earlier 73 Scalable Decision Tree Induction Methods • RainForest (VLDB’98 — Gehrke, Ramakrishnan & Ganti) Builds an AVC-list (attribute, value, class label) • BOAT (PODS’99 — Gehrke, Ganti, Ramakrishnan & Loh) Uses bootstrapping to create several small samples 74 Presentation of Classification Results January 24, 2012 Data Mining: Concepts and Techniques 75 Visualization of a Decision Tree in SGI/MineSet 3.0 January 24, 2012 Data Mining: Concepts and Techniques 76 Interactive Visual Mining by Perception-Based Classification (PBC) January 24, 2012 Data Mining: Concepts and Techniques 77 SUMMARY OF DECISION TREE 78 Decision Tree Classification Task 79 Apply Model to Test Data 80 Apply Model to Test Data 81 Apply Model to Test Data 82 Apply Model to Test Data 83 Apply Model to Test Data 84 Decision Tree Induction • Many Algorithms: Hunt’s Algorithm (one of the earliest) CART ID3, C4.5 SLIQ,SPRINT 85 Q&A 86