An organizational co evolutionary algorithm for classification

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  • evolutionary and selection operators are used to simulate the interacting process among organization.
  • evolutionary and selection operators are used to simulate the interacting process among organization.
  • evolutionary and selection operators are used to simulate the interacting process among organization.
  • evolutionary and selection operators are used to simulate the interacting process among organization.
  • evolutionary and selection operators are used to simulate the interacting process among organization.
  • evolutionary and selection operators are used to simulate the interacting process among organization.
  • evolutionary and selection operators are used to simulate the interacting process among organization.
  • An organizational co evolutionary algorithm for classification

    1. 1. An Organizational Co-evolutionary Algorithm For Classification Developed By: Badar Munir National University of Computer & Emerging Sciences, Islamabad
    2. 2. Index1. Abstract2. Introduction3. Reference Techniques4. Proposed Technique5. Results6. Conclusion7. Future idea National University of Computer & Emerging Sciences, Islamabad
    3. 3. AbstractOCEC is inspired from human interacting process.- It uses the concept of Multi Poulation.- It evolves individuals of population, individualsthat have same class arranges them inorganization.Determines the fitness of each organization byCalculating its - Significance of each attribute. - # of attributes in it National University of Computer & Emerging Sciences, Islamabad
    4. 4. Abstract- Rules are extracted when evolutionary processends.- Generalized rules are by merging rules.- OCEC performs better than other EA basedclassification algorithms and has lesscomputational complexity. National University of Computer & Emerging Sciences, Islamabad
    5. 5. Co-evolutionary Algorithm- EA are based on the process Natural Selection.- When ever it is applied on engineeringproblems it gives satisfactory results.- Co-evolutionary algorithm is Multi-Population.- In Co-evolutionary algorithms individuals ofspecies-I competes/ cooperates with species-II.Best individual from both them is selected andcopied to next generation. National University of Computer & Emerging Sciences, Islamabad
    6. 6. Co-evolutionary AlgorithmTwo types of Co-evolutionary algorithms are:- Competitive- Cooperative National University of Computer & Emerging Sciences, Islamabad
    7. 7. ClassificationClassification is a technique in which• # possible inputs, #attributes in input,• Range of attribute values• Output Classes are already known. NAME RANK YEARS TENURED Mike Assistant Prof 3 no Mary Assistant Prof 7 yes Bill Professor 2 yes Jim Associate Prof 7 yes Dave Assistant Prof 6 no Anne Associate Prof 3 no National University of Computer & Emerging Sciences, Islamabad
    8. 8. Classification- We divide the dataset into Training Test Data Data Input Data National University of Computer & Emerging Sciences, Islamabad
    9. 9. Classification Classification Algorithms Training DataNAME RANK YEARS TENURED Classifier (Model)Mike Assistant Prof 3 noMary Assistant Prof 7 yesBill Professor 2 yesJim Associate Prof 7 yes IF rank = ‘professor’Dave Assistant Prof 6 no OR years > 6Anne Associate Prof 3 no THEN tenured = ‘yes’ National University of Computer & Emerging Sciences, Islamabad
    10. 10. ClassificationOur aim in classification is to develop- Generalized rules instead of Specific
    11. 11. ClassificationCases results during the evaluation ofclassification:Underflow & Overflow
    12. 12. Reference Techniques1- Michigan Approach 9- XCS2- Pittsburgh approach 10- GEP3- Chonnei Algorithm 11- DMEL4- GABIL Approach 12- EVOPROL5- COGIN 13- SIA6- JOINGA 14- ESIA7- REGAL 15- EENCL8- G-Net 16- EPNET National University of Computer & Emerging Sciences, Islamabad
    13. 13. Michigan Approach-Maintains a population of individual ruleswhich compete with each other for space andpriority in a population.- It is not a good approach because it cannotfind best solution in complex problems insteadit converges rapidly. National University of Computer & Emerging Sciences, Islamabad
    14. 14. Pittsburgh Approach-Maintains a population of variable-length ruleset which compete with each other with respectto performance on a domain task.- computational cost for complex problems istoo high. National University of Computer & Emerging Sciences, Islamabad
    15. 15. GABIL Approach- GABIL continuously learns and refinesclassification rules by interacting withenvironment.- For rules refinement it uses Genetic Algorithm National University of Computer & Emerging Sciences, Islamabad
    16. 16. COGIN Approach- CONGIN is a inductive approach that uses GA.- It promotes Competitive or Predator type COEbetween classification nichie’s. National University of Computer & Emerging Sciences, Islamabad
    17. 17. JOINGA Approach- CONGIN is a inductive approach that uses GA.- It uses Cooperative or Symbiotic type COEbetween classification nichie’s.- It is used for Multi-Model classification. National University of Computer & Emerging Sciences, Islamabad
    18. 18. REGAL Approach- It is a distributed GA based approach designedfor learning first-order logic conceptsdescription from examples. National University of Computer & Emerging Sciences, Islamabad
    19. 19. G-NET Approach-G-NET is a descendant of REGAL thatconsistently achieves better performance. National University of Computer & Emerging Sciences, Islamabad
    20. 20. Organizational co-evolutionary (OCEC)- OCEC copies COE model of MultiplePopulations- It organizes the individuals in a sets calledorganizations.- Focusing on extracting rules from individuals &organization.- It does not focus on making organizations butit focus on simulating interacting process amongorganization.- It is bottom-up approach. National University of Computer & Emerging Sciences, Islamabad
    21. 21. Organizational co-evolutionary (OCEC)- OCEC is based on organizations. • Organization 1 • Organization 2 • Organization3 • Organization 4 National University of Computer & Emerging Sciences, Islamabad
    22. 22. Organization?- An organization is a set of instances that havesame class- Intersection between organizations is empty. Org1 Π Org2 = Ø Outlook Temp Humidity Wind Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild High False Yes Rainy Cool Normal False Yes* Each instance of an org is called Member of org. National University of Computer & Emerging Sciences, Islamabad
    23. 23. Organization?- If all members of org have the same value forattribute A , then A is a Fixed-Value Attribute.Suppose A’ is a fixed-value attribute that satisfythe conditions required for rule extraction, thenA’ is a Useful Attribute. The fixed-value attributeset of org is labeled as Forg, and the usefulattribute set is labeled as Uorg- Useful attribute is significant because itextracts rule. National University of Computer & Emerging Sciences, Islamabad
    24. 24. Organization?Wind  Forg1 & Uorg1 (Org2)Outlook  Uorg2 (Org2)Temp  Forg2 & Uorg2Humidity  Forg2 & Uorg2 Outlook Temp Humidity Wind Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild High False Yes Rainy Cool Normal false Yes National University of Computer & Emerging Sciences, Islamabad
    25. 25. Classification of OrganizationsClassification of organizations are:- Normal organization- Trivial Organization- Abnormal organization National University of Computer & Emerging Sciences, Islamabad
    26. 26. Normal Organization- It has more than one members- Has non-empty useful attributes set Outlook Temp Humidity Wind Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild High False Yes Rainy Cool Normal False Yes- It is denoted as ORGN National University of Computer & Emerging Sciences, Islamabad
    27. 27. Trivial Organization- It has only one members &- All attributes of a member are useful. Outlook Temp Humidity Wind Play Sunny Hot High True No Overcast Hot High False Yes- It is denoted as ORGT National University of Computer & Emerging Sciences, Islamabad
    28. 28. Abnormal Classification- It is an organization with empty usefulattributes. Outlook Temp Humidity Wind Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild High True Yes Rainy Cool Normal False Yes- It is denoted as ORGA National University of Computer & Emerging Sciences, Islamabad
    29. 29. Organization Records Organization keeps record of- Member list- Attribute type- Organization type- Member class- Fitness of organization National University of Computer & Emerging Sciences, Islamabad
    30. 30. Fitness of OrganizationFitness of an organization is calculated as:- # of members- # of useful attributes- National University of Computer & Emerging Sciences, Islamabad
    31. 31. Data RepresentationOCEC can handle both - Nominal & - Continuous data Outlook Temp Humidity Wind Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild High False Yes Rainy Cool Normal false Yes National University of Computer & Emerging Sciences, Islamabad
    32. 32. Knowledge Representation- A is a set of attributes- Each attribute has range of values. Outlook Temp Humidity Wind Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild High False Yes Rainy Cool Normal false Yes National University of Computer & Emerging Sciences, Islamabad
    33. 33. Knowledge Representation- Instance Space I is the cartesian product of setof attributes Outlook Temp Humidity Wind Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild High False Yes Rainy Cool Normal false Yes National University of Computer & Emerging Sciences, Islamabad
    34. 34. Knowledge Representation- C is a set of classes- Each member is Outlook Temp Humidity Wind Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild High False Yes Rainy Cool Normal false Yes National University of Computer & Emerging Sciences, Islamabad
    35. 35. Rule RepresentationRules are represented in IF <condition> THEN <class>Each term in condition is triple: Attribute, operator, value* Rules are extracted when evolutionary process Ends National University of Computer & Emerging Sciences, Islamabad
    36. 36. Working of (OCEC)- OCEC during COE process generates a of set ofexamples and at the end of COE it generates setof rules. if Temp = Mild and Outlook= Sunny then Class = Play Tennis National University of Computer & Emerging Sciences, Islamabad
    37. 37. Working of (OCEC)- Inclusion or exclusion of attribute from a ruledepends upon the Significance of the attribute.- EA Method is devised for determining theSignificance of the attribute.- on the basis of attribute significance Fitnessfunction of organization is defined. National University of Computer & Emerging Sciences, Islamabad
    38. 38. Working of (OCEC)- EA Method is devised for determining theSignificance of the attribute.- On the basis of attribute significance Fitnessfunction of organization is defined. National University of Computer & Emerging Sciences, Islamabad
    39. 39. Evolutionary Operators (OCEC)- Migrating Operator- Exchanging Operator- Merging OperatorTraditional operators such as mutation andcrossover are not used. National University of Computer & Emerging Sciences, Islamabad
    40. 40. Migrating Operators (OCEC)- 2 parent organizations are selected- n members are selected from either parentand are migrated to child’s 1 2 3 4 5 6 7 8 1 2 3 4 5 1 2 3 National University of Computer & Emerging Sciences, Islamabad
    41. 41. Exchanging Operators (OCEC)- 2 org’s are randomly selected from Populationorg1 & org2 Parent Parent ORG1 ORG2 Child- Off- ORGc1 ORGc2 National University of Computer & Emerging Sciences, Islamabad
    42. 42. Exchanging Operators (OCEC)- n members from each parent org1 arerandomly selected and exchanged- Two child organization orgc1 & orgc2 1 2 3 4 5 6 7 8 1 6 7 8 5 1 2 3 National University of Computer & Emerging Sciences, Islamabad
    43. 43. Exchanging Operators (OCEC)- Two child organization orgc1 & orgc2- Precondition is: |orgp1|>1 and |orgp2|>1 1 ≤ n < MIN{|orgp1|, |orgp2|} National University of Computer & Emerging Sciences, Islamabad
    44. 44. Merging Operators- 2 org’s are randomly selected from Populationorgp1 & orgp2 Parent Parent ORG1 ORG2 Child- ORGc1 National University of Computer & Emerging Sciences, Islamabad
    45. 45. Merging Operators (OCEC)- n members from each org1 are randomlyselected and merged.- One child organization orgc1 & orgc2 1 2 3 4 5 6 7 8 1 2 7 8 National University of Computer & Emerging Sciences, Islamabad
    46. 46. Selection Operators (OCEC)- Tournament Selection Mechanism is used. National University of Computer & Emerging Sciences, Islamabad
    47. 47. Rule Extraction From Organization-Rules are extracted from organizations whenEvolutionary process ends.- Rules are extracted on the basis usefulattributes.- Each useful attribute becomes TERM (part ofcondition). if temp=hot then play = yes National University of Computer & Emerging Sciences, Islamabad
    48. 48. Performance Evaluation of OCEC-Multiplexer problem- Radar Target Recognition Problem.-All results shows that OCEC has - Higher prediction accuracy - Low computational cost. National University of Computer & Emerging Sciences, Islamabad
    49. 49. Scalability Evaluation of OCEC-Scalability of OCEC is evaluated on syntheticsets. - trainging exampels increases from 1lac to 10 Million - attributes are increases from 9 to 400. - results shows that I achieves good scalability. National University of Computer & Emerging Sciences, Islamabad
    50. 50. EVALUATION OF OCEC’S EFFECTIVENESSA. Multiplexer Problemso Multiplexer problems were introduced to the machine learning community by Wilson in 1987, and have often been used to evaluate the performance of learning classifier systems National University of Computer & Emerging Sciences, Islamabad
    51. 51. EVALUATION OF OCEC’S EFFECTIVENESSB. Experimental Resultso The 20- and 37-multiplexer problems are usedo The training set of the 20-multiplexer problem has 3000 examples, and that of the 37-multiplexer problem has 15 000 exampleso The test set of each problem has 100 000 exampleso The parameter N is set to 10% of the number of the training set, and n National University of Computer & Emerging Sciences, Islamabad
    52. 52. EVALUATION OF OCEC’S EFFECTIVENESSThe evolutionary process of OCEC for the 20-multiplexerproblem National University of Computer & Emerging Sciences, Islamabad
    53. 53. EVALUATION OF OCEC’S EFFECTIVENESSThe evolutionary process of OCEC for the 37-multiplexerproblem National University of Computer & Emerging Sciences, Islamabad
    54. 54. Coding OutputThe evolutionary process of OCEC for the 37-multiplexerproblem National University of Computer & Emerging Sciences, Islamabad
    55. 55. Coding Output National University of Computer & Emerging Sciences, Islamabad
    56. 56. Coding Output National University of Computer & Emerging Sciences, Islamabad
    57. 57. Comparison between OCEC & EA- OCEC is based on organization whiletraditional EA are based in individuals.-OCEC has bottom-up searching mechanismwhile EA has top-down searching mechanism- the benefit of using organization is that I doesnot generate meaningless rules.- OCEC has higher prediction accuracy and lowcomputational cost. National University of Computer & Emerging Sciences, Islamabad
    58. 58. Conclusion- It is best tool for data mining.- It has low computational cost- It performs well in a complex, huge dataset ofindividuals.- On high scalability it performs better thanother techniques. National University of Computer & Emerging Sciences, Islamabad
    59. 59. Future IDEA-If we use a Floating Point Fitness Function thenit will give us better result in Scientificapplications. National University of Computer & Emerging Sciences, Islamabad

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