1. R for finding the non-dominated rules in multi-objective
optimizationBo-Han Wu Jan 27, 2014TaiwanRUserGroup/MLDMMonday
2. GoogleWu Bo-Han [email protected] 3. Outline
Introduction Classification rule Accuracy Comprehensibility
Interestingness Multi-objective optimization Non-dominated rules
SPEA2 Case study Wu Bo-Han [email protected] 4. Data
growingWu Bo-Han [email protected] 5. Introduction Facing
the age of data explosion, the amount of data is increasing very
fast in databases. Those data normally include hidden knowledge,
and they can be used to improve the decision-making process of any
kinds of company. Wu Bo-Han [email protected] 6.
Classification rule Classification rule mining is a common
technology in data mining. From the historical data, rule can be
generalized to classify unknown samples or predict the future.Wu
Bo-Han [email protected] 7. Classification rule IF AND THEN
Example: IF SexMale AND Location = Taipei THEN Product beerWu
Bo-Han [email protected] 8. Classification rule Traditional
mining techniques mostly focus on accuracy and usually generate
lots of rules that are hard to choose meaningful ones from. In
order to select optimally meaningful rules, accuracy,
comprehensibility and interestingness are employed as three
objectives.Wu Bo-Han [email protected] 9. Accuracysup( A
& C ) A(R) sup( A ) is the support for the rule R represents
the support for the antecedent of rule R Wu Bo-Han
[email protected] 10. ComprehensibilityNc ( R) C( R) 1 Mc
Nc(R)is the number of conditions in the rule Mc is the maximum
number of conditions that a rule can have Wu Bo-Han
[email protected] 11. Interestingness sup( A & C ) sup(
A & C ) sup( A & C ) I (R) 1 sup( A ) sup( C ) D 1 1 gives
the probability of generating the rule depending on the antecedent
part gives the probability of generating the rule depending on the
consequent part gives the probability of generating the rule
depending on the whole data-setWu Bo-Han [email protected]
12. Multi-objective optimization Low price and high performance
90%Performance40% 10k NondominatedsolutionPrice100kWu Bo-Han
[email protected] 13. Multi-objective optimization Low price
and high performance 90%453 2Performance40%110k
NondominatedsolutionPrice100kWu Bo-Han [email protected] 14.
Multi-objective optimization Low price and high performance 90%453
2Performance40% Nondominatedsolutionset
Nondominatedsolution110kPrice100kWu Bo-Han [email protected]
15. Multi-objective optimization However, traditional methods
handle multiobjective problems by converting them into a single
objective problem. But this approach can not guarantee to find
optimal solutions for multiple objectives.Wu Bo-Han
[email protected] 16. SPEA2 SPEA2 is designed by the concept
"survival of the fittest" from natural evolution. The work intended
to improve quality of individuals from solution space in each
generation. SPEA2 used the strategy of selection, crossover and
mutation to retain the best individuals and discard worst ones. Wu
Bo-Han [email protected] 17. SPEA2Wu Bo-Han
[email protected] 18. SPEA2Initial populationEmpty
archiveIndividual Wu Bo-Han [email protected] 19. SPEA2Wu
Bo-Han [email protected] 20. Non-dominatedWu Bo-Han
[email protected] 21. Non-dominated solutionWu Bo-Han
[email protected] 22. Non-dominated solution set EFWu Bo-Han
[email protected] 23. SPEA2Individual Nod-dominated
Individual Wu Bo-Han [email protected] 24. SPEA2Wu Bo-Han
[email protected] 25. SPEA2Individual Nod-dominated
Individual Wu Bo-Han [email protected] 26. SPEA2 Truncation
operatorIndividual Nod-dominated Individual Wu Bo-Han
[email protected] 27. SPEA2Wu Bo-Han
[email protected] 28. SPEA2Wu Bo-Han
[email protected] 29. SPEA2241 3Wu Bo-Han
[email protected] 30. SPEA2Wu Bo-Han
[email protected] 31. SPEA2 Recombination =
10101101011001100100010010111 =
01100110010111001011101101101Mutation =
01100101011001100100010010111 = 10010101011001100100010010111Wu
Bo-Han [email protected] 32. SPEA24321Wu Bo-Han
[email protected] 33. Non-dominated rules Three
objectivesIFSexMale ANDLocation= Taipei THENProduct beerA=0.333333
C=0.875000 I=0.080000 Accuracy Comprehensibility
InterestingnessNondominatedrules Wu Bo-Han [email protected]
34. Case study Transactiondataofaninsurancebrokercompany Date:2005
2006Attribute Gender Occupation Paymentfrequency Salesmethods
Payment methods Location Data source Company ProductAttribute
valueindex () () Wu Bo-Han [email protected] 35. Case
studyDataCleaningDatatransactionTrainingdataand TestdataExample:
Male01 Female10Accuracy DatatransactionSPEA2Comprehensibility
InterestingnessExample: 01 Male 10FemaleWu Bo-Han
[email protected] 36. Case study SPEA2 RuleMing.r Objective
Functions.r SPEA2 Functions.rTruncation.rCrossover.rMutation.rWu
Bo-Han [email protected] 37. Case study Non-dominated rules
Sales methods= AND Data source= AND Company= THEN Product= Payment
methods= AND Data source= AND Company= THEN Product= Payment
frequency= AND Data source= Company=Wu Bo-Han
[email protected] 38. Case study Non-dominated rules Sales
methods= AND Data source= AND Company= THEN Product= Wu Bo-Han
[email protected] 39. Thanks for your listeningWu Bo-Han
[email protected]
