L7classification2

L7classification2 - Classification Lecture Notes (2)...

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Classification Lecture Notes (2) (cse352) Review, Training, Testing, Predictive Accuracy Professor Anita Wasilewska
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Classification Data Data format: a data table with key attribute removed. Special attribute- class attribute must be distinguished 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
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Classification (Training ) Data with objects rec Age Income Student Credit_rating Buys_computer (CLASS) 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
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CHARACTERISTIC DESCRIPTIONS (Review) Example: Some of the characteristic descriptions of the concept C with description : buys_computer= no are Age=<= 30 & income=high & student=no & credit_rating=fair Age=>40& income=medium & student=no & credit_rating=excellent Age=>40& income=medium Age=<= 30 student=no & credit_rating=excellent
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Characteristic Formula (Review) Any formula (of a proper language) of a form IF concept desciption THEN characteristics is called a characteristic formula Example: IF buys_computer= no THEN income = low & student=yes & credit=excellent IF buys_computer= no THEN income = low & credit=fair
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Characteristic Rule (Review) A characteristic formula IF concept desciption THEN characteristics is called a characteristic rule (for a given database) if and only if it is TRUE in the given database, i.e. {r: concept description} /\{r: characteristics} = not empty set
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Characteristic Rule (Review) EXAMPLE: The formula IF buys_computer= no THEN income = low & student=yes & credit=excellent is a characteristic rule for our database because { r: buys_computer= no } = { r1,r2, r6, r8, r16 }, { r: } = { r6,r7 } and { r1,r2, r6, r8, r16 } /\ { r6,r7 } = not emptyset
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Characteristic Rule (Review) EXAMPLE: The formula IF buys_computer= no THEN income = low & credit=fair Is NOT a characteristic rule for our database because { r: buys_computer= no } = { r1,r2, r6, r8, r16 }, { r: } = { r5, r9 } and { r1,r2, r6, r8, r16 } /\ { r5,r9 } = emptyset
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(Review) Discrimination is the process which aim is to find rules that allow us to discriminate the objects (records) belonging to a given concept (one class ) from the rest of records ( classes) DISCRIMINANT RULES have a form:
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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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L7classification2 - Classification Lecture Notes (2)...

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