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Mining Changes of Classification by Correspondence Tracing

Mining Changes of Classification by Correspondence Tracing. Ke Wang Senqiang Zhou Simon Fraser University {wangk, szhoua}@cs.sfu.ca. Wai Chee Ada Fu Jeffrey Xu Yu The Chinese University of Hong Kong adafu@cs.cuhk.edu.hk yu@se.cuhk.edu.hk. The Problem.

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Mining Changes of Classification by Correspondence Tracing

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  1. Mining Changes of Classification by Correspondence Tracing Ke Wang Senqiang ZhouSimon Fraser University{wangk, szhoua}@cs.sfu.ca Wai Chee Ada Fu Jeffrey Xu YuThe Chinese University of Hong Kongadafu@cs.cuhk.edu.hkyu@se.cuhk.edu.hk

  2. The Problem • Mining changes of classification patterns as the data changes • What we have: old classifier and new data • What we want: the changes of classification characteristics in the new data members with a large family  shop frequently. members with a large family shop infrequently. Example

  3. Targets • State the changes explicitly • Simply comparing old and new classifiers does not work • Distinguish the important changes, otherwise users could be over-whelmed • Interested in the changes causing the change of classification accuracy.

  4. Related Work • [GGR99]: transfer two classifiers into a common specialization and the changes are measured by the amount of work required for such transfer. • Human users hardly measure the changes in this way • Have not addressed the primary goal: accuracy

  5. Related Work (Cont.) • [LHHX00]: extract changes by requiring the new classifier to be similar to the old one. • Using the same splitting attributes or the same splitting in the decision tree construction. • Put a severe restriction on mining important changes.

  6. Our Approach: Basic Idea • For each old rule o, trace the corresponding new rules in the new classifier through the examples they both classify Example: n1 and n2 are corresponding rules of o. New rule n1 New rule n2 Old rule o New data set

  7. The Algorithm • Input: old classifier and new data • Output: the changes of classification patterns Identify corresponding new rules for each old rule Present changes Build new classifier Step 3 Step 2 Step 1

  8. Identify The Corresponding Rules • Given an old rule o: • Collect the examples classified by o • For each such example, identify the new rule n that classifies it • characteristic change: O <n1,n2, …, nk> New rule n1 Old rule o New rule n2

  9. Any Important Changes? • Given O <n1,n2, …, nk>, which changes are more important? • Hint: users usually are interested in the changes causing the change of classification accuracy. • Basic idea: measure the importance of changes based on the estimated accuracy of rules on future data

  10. Pessimistic Error Estimation • Consider an old rule o that classifies N examples ( in new data set) with E wrongly • Observed error rate: E/N • How about the error rate in the whole population? • Given an confidence level CF, the upper bound of error rate is UCF(N,E) • Details in [Q93]

  11. Estimating Quantitative Change • Consider a rule pair <o, ni>, while o(No, Eo), ni(Nn, En) Estimate the error rates for both rules Calculate the decrease of error Quantitative change Calculate the increase of accuracy

  12. An Example Consider <o, n1, n2> o: A4=1  C3, (N=7, E=4) n1: A3=1A4=1  C3, (N=5, E=0) 3 classified by o n2: A3=2A4=1  C2 , (N=6, E=0) 4 classified by o Assume the new data set has 18 examples and CF=25%. Consider the quantitative changes of <o, n1> The estimated error rate of o is: UCF(7, 4) = 75% The estimated error rate of n1 is: UCF(5, 0) = 24% The decrease of error rate: 75% - 24% = 51% The increase of accuracy (o,n1) = (3/18)*51%=8.5% The increase of accuracy (o,<n1,n2>) =8.5%+12%=20.5%

  13. Types of Changes • Global changes: both characteristics change and quantitative change are large. • Alternative changes: characteristics change is large but its quantitative change is small.

  14. Types of Changes (Cont.) • Target changes: similar characteristics but different classes (targets) • Interval changes: shift of boundary points due to the emerging of new cutting points.

  15. Experiments • Two data sets: • German Credit Data from UCI repository [MM96] • IPUMS Census Data [IPUMS] • Goal: to verify if our algorithm can find the important changes “supposed” to be found

  16. Methodologies • For German Credit data: • Plant some changes to original data and check if the proposed method finds them. • For IPUMS census data: • The proposed method is applied to find the changes across years or different sub-populations. • Classifiers are built using C4.5 algorithm

  17. Summaries of German Data • Data description: • 2 classes: bad and good • 20 attributes, 13 categorical • 1000 examples: 700 are “good” • Changes planted: • Target change • Interval change • Etc.

  18. Planting Target Change Personal-status = single-male, Foreign = no  Credit = good (23, 0) Consider the examples classified by the old rule. Changes planted: if ( Liable-people=1 ) then change the class from good to bad 12 examples changed

  19. The Changes Found Personal-status = single-male, Foreign = no  Credit = good Personal-status = single-male, Liable-people <=1, Foreign = no  Credit = bad ( = 0.48%) Liable-people >1, Foreign = no Credit = good( = 0.54%)

  20. Planting Interval Change Status = 0DM, Duration > 11, Foreign = yes  Credit = bad (164, 47) Consider the examples classified by the old rule. Changes planted: Increase the Duration value by 6 (months) for each example classified by the old rule. 164 examples changed

  21. The Changes Found Status = 0DM, Duration > 11, Foreign = yes  Credit = bad Status = 0DM,Duration > 16, Foreign = yes  Credit = bad( = 1.20%)

  22. Summaries of IPUMS Data • Take “vetstat” as class • 3 data sets: 1970, 1980 and 1990. • Each data set contains the examples for several races. • The proposed method is applied to find the changes across years or different sub-populations.

  23. Interesting Changes Found A. 1970-black vs 1990-black 35<age ≤54 Vetstat=yes 40<age ≤72, sex=male Vetstat=yes( = 1.20%) B. 1990-black vs 1990-chinese 40<age ≤72, sex=male Vetstat=yes bplg =china, incss ≤5748 Verstat=no( = 4.56%)

  24. Conclusion • Extracting changes from potentially very dissimilar old and new classifiers by correspondence tracing • Ranking the importance of changes • Presenting the changes • Experiments on real-life data sets

  25. References • [GGR99] V. Ganti, J. Gehrke, and R. Ramakrishnan. A framework for measuring changes in data characteristics. In PODS, 1999 • [IPUMS] http://www.ipums.umn.edu/. • [LHHX00] B. Liu,W. Hsu, H.S. Han, and Y. Xia. Mining changes for real-life applications. In DaWak, 2000

  26. References (Cont.) • [MM96] C.J. Merz and P. Murphy. UCI repository of machine learning databases • [Q93] J.R. Quinlan. C4.5: programs for maching learning. 1993

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