L6classification1

L6classification1 - Classification Lecture Notes (1)...

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Unformatted text preview: Classification Lecture Notes (1) (cse352) • PART ONE: Supervised learning (Classification) • Data format: training and test data • Concept, or class definitions and description • Rules learned: characteristic and discriminant • Supervised learning = classification = classification process = building a classifier. • Classification algorithms • Evaluating predictive accuracy of a classifier: the most common methods • Unsupervised learning = clustering • Clustering methods Part 2: Classification Algorithms (Models, Basic Classifiers) • Decision Trees (ID3, C4.5) – descriptive • Neural Networks- statistical • Bayesian Networks - statistical • Rough Sets - descriptive • Genetic Algorithms – descriptive or statistical- but mainly an optimization method • Classification by Association – descriptive – data Mining Algorithm Part 3: Other Classification Methods • k-nearest neighbor classifier • Case-based reasoning • Fuzzy set approaches • Markov Chains PART 1: Learning Functionalities (1) Classification Data • Data format: a data table with key attribute removed. Special attribute- class attribute must be distinguished, • Here it is Buys_computer age income student credit_rating buys_computer <=30 high no fair no <=30 high no excellent no 30… 40 high no fair yes >40 medium no fair yes >40 low yes fair yes >40 low yes excellent no 31… 40 low yes excellent yes <=30 medium no fair no <=30 low yes fair yes >40 medium yes fair yes <=30 medium yes excellent yes 31… 40 medium no excellent yes 31… 40 high yes fair yes >40 medium no excellent no Part 1: Learning Functionalities Classification Data with objects rec Age Income Student Credit_rating Buys_computer r1 <=30 High No Fair No r2 <=30 High No Excellent No r3 31…40 High No Fair Yes r4 >40 Medium No Fair Yes r5 >40 Low Yes Fair Yes r6 >40 Low Yes Excellent No r7 31…40 Low Yes Excellent Yes r8 <=30 Medium No Fair No r9 <=30 Low Yes Fair Yes r10 >40 Medium Yes Fair Yes r11 <-=30 Medium Yes Excellent Yes r12 31…40 Medium No Excellent Yes r13 31…40 High Yes Fair Yes r14 >40 Medium No Excellent No Learning Functionalities (2) Concept or Class Definitions • Syntactically a Concept or a Class is defined by the concept ( class ) attribute c and its value v • Semantically Concept or Class – is any subset of records. • Concept (Class) syntactic description is written as : c=v • Semantically , a concept, or a class defined by the attibute c is the set of all records for which the attribute c has a value v . Learning Functionalities (3) Concept or Class definitions • Example: Set of records { r1, r2, r6, r8, r14} of the table on the previous slide is a CONCEPT, or a CLASS It is defined syntactically by the class attribute buys_computer and its value no; Concept (class) { r1, r2, r6, r8, r14} description is: buys_computer= no because { r1, r2, r6, r8, r14} = {r: buys_computer= no } We shortly say that it is a class buys_computer= no Learning Functionalities (4)...
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This note was uploaded on 01/25/2012 for the course CSE 352 taught by Professor Wasilewska,a during the Fall '08 term at SUNY Stony Brook.

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L6classification1 - Classification Lecture Notes (1)...

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