LCS研究概述.ppt

ResearchonLearningClassifierSystem 学习分类系统研究概述 Outline IntroductionDefinition HistoryBasicIdeaofLCSTypes ApproachesOurCurrentProgressWhatwehavedone HotIssuesandFutureDirectionWhatisapromisingfutureresearchdirection Outline IntroductionDefinition HistoryBasicIdeaofLCSTypes ApproachesOurCurrentProgressWhatwehavedone HotIssuesandFutureDirectionWhatisapromisingfutureresearchdirection Adaptiverule basedproductionsystemSetofrulesTrialanderror 强化学习通过环境的反馈来调整自身的行为Survivalofthefittest 遗传算法以遗传算法来探索 发现规则 IntroductiontoLCS RuleA RuleB ReinforcementLearning Environment RLAgent action Reward State GeneticAlgorithm Population 2 Population Population NewIndividual GoodIndividual 1 Individual Selection Reproduction ComponentsinLCS ComponentsinLCS ComponentsinLCS BriefViewofLCSHistory 1971Holland首次提出分类系统概念 1978Holland正式确立学习分类系统名称 并提出大概框架 1988Holland定义标准框架 太复杂 LCS研究停滞 1995Wilson进一步提出XCS 从此LCS的研究进入新的阶段 1994Wilson简化了标准LCS 提出更易实现的ZCS 1998Stolzmann提出不同于传统LCS的A LCS 新的方向 Relative ConferencesandMagazinesGeneticandEvolutionaryComputationConference GECCO InternationalWorkshoponLearningClassifierSystems IWLCS SEAL EvolutionaryComputation IEEETransactiononEC PapersandApplicationsH Ishibuchi FuzzyGenetics BasedMachineLearning SEAL2012 PierLucaLanzi XCSwithAdaptiveActionMapping SEAL2012 R Urbanowicz Instance LinkedAttributeTrackingandFeedbackforMichigan StyleSupervisedLearningClassi erSystems GECCO2012 M Iqbal ExtractingandUsingBuildingBlocksofKnowledgeinLearningClassi erSystems GECCO2012 Outline IntroductionDefinition HistoryBasicIdeaofLCSTypes ApproachesOurCurrentProgressWhatwehavedone HotIssuesandFutureDirectionWhatisapromisingfutureresearchdirection BriefViewofLCSHistory 1971Holland首次提出分类系统概念 1978Holland正式确立学习分类系统名称 并提出大概框架 1988Holland定义标准框架 太复杂 LCS研究停滞 1995Wilson进一步提出XCS 从此LCS的研究进入新的阶段 1994Wilson简化了标准LCS 提出更易实现的ZCS 1998Stolzmann提出不同于传统LCS的A LCS 新的方向 Holland sLCS 缺陷 1 无节制使用遗传算法2 桶队列算法的依赖性 规则条件 动作 预测匹配集 M 动作选择动作集 A BriefViewofLCSHistory 1971Holland首次提出分类系统概念 1978Holland正式确立学习分类系统名称 并提出大概框架 1988Holland定义标准框架 太复杂 LCS研究停滞 1995Wilson进一步提出XCS 从此LCS的研究进入新的阶段 1994Wilson简化了标准LCS 提出更易实现的ZCS 1998Stolzmann提出不同于传统LCS的A LCS 新的方向 Wilson sXCS 最重要的改进部分 重新定义了适应度计算方法Holland sLCS 规则的权值Wilson sXCS 引入了新的参数通过计算精确度来度量遗传算法 BriefViewofLCSHistory 1971Holland首次提出分类系统概念 1978Holland正式确立学习分类系统名称 并提出大概框架 1988Holland定义标准框架 太复杂 LCS研究停滞 1995Wilson进一步提出XCS 从此LCS的研究进入新的阶段 1994Wilson简化了标准LCS 提出更易实现的ZCS 1998Stolzmann提出不同于传统LCS的A LCS 新的方向 Stolzmann sACS Model FreeLCSsZCS XCSNoknowledgeaboutresultofactionsModel BasedLCSAnticipatoryclassifiersystems ACS 1998 Anticipatorylearningclassifiersystems ACS2 2000 Knowledgeaboutresultofactions TwoApproaches 1 MichiganApproach Searchforgoodrules 2 PittsburghApproach Searchforagoodrulecombination Champions Goodplayers Goodcooperation MichiganApproach FitnessEvaluationofEachRuleDirectOptimizationofRulesNewrulesaregeneratedfromgoodrulesIndirectSearchforaGoodRuleSetAsetofgoodrulesisnotnecessarilyagoodruleset RuleA RuleC RuleE RuleG RuleB RuleD RuleF RuleH PittsburghApproach FitnessEvaluationofEachSubRuleSetDirectOptimizationofRuleSetsNewrulesetsaregeneratedfromgoodrulesetsIndirectSearchforGoodRulesGoodrulesinapoorrulesetcannotsurvive RuleARuleBRuleC RuleDRuleERuleF RuleGRuleHRuleI RuleJRuleKRuleL Michigan PittsburghHybridApproach H Ishibuchietal HybridizationofFuzzyGBMLApproachesforPatternClassificationProblems IEEET SMCPartB 2005 Outline IntroductionDefinition HistoryBasicIdeaofLCSTypes ApproachesOurCurrentProgressWhatwehavedone HotIssuesandFutureDirectionWhatisapromisingfutureresearchdirection ImprovementofLCS Sub LCS LCSE RuleA RuleC RuleE RuleB RuleB RuleD RuleF RuleA ability readability Sub LCS Sub LCS EnsembleMethod ParallelensembleBagging Randomsubspace Randomforest creatediversebaselearnersbyintroducingrandomnessSequentialensembleAdaboost createbaselearnersbycomplementarity LCSE LCSEnsemble Bagging LCSE LCSEnsemble Boosting CompactRuleSet Supposesimplestconditions 2 DProblem 32 9rules4 DProblem 34 81rules6 DProblem 36 729rules8 DProblem 38 6 561rules10 DProblem 310 59 049rules lackofreadabilitytraditionalCRAistoocomplicated CompactRuleSet YangGao LeiWu JoshuaZhexueHuang EnsembleLearningClassifierSystemandCompactRuleset In Proceedingsofthe6thInternationalConferenceonSimulatedEvolutionandLearning LNCS4247 pp 42 49 2006 CompactRuleSet YangGao LeiWu JoshuaZhexueHuang EnsembleLearningClassifierSystemandCompactRuleset In Proceedingsofthe6thInternationalConferenceonSimulatedEvolutionandLearning LNCS4247 pp 42 49 2006 LCSInLearningApplications Preprocess 训练数据的缺失 噪声 LCS用于处理不同的学习情况 L LU U 显然可以 效果明显 Semi supervised Classification Clustering 训练数据 HandlingMissingData D GuandY Gao Incrementalgradientdescentimputationmethodformissingdatainlearningclassi ersystems inProceedingsofGECCO 05 2005 pp 72 73 HandlingMissingData D GuandY Gao Incrementalgradientdescentimputationmethodformissingdatainlearningclassi ersystems inProceedingsofGECCO 05 2005 pp 72 73 usetherelationshipamongvariablestoestimatethemissingvalue ClusteringwithLCS Becausethefinalpopulationrulesetsuggestssomeimportantpatternsinthedataset LCSEcanpredicttheunforeseencasescorrectly Nocleardefinitionofwhatshouldbeinacluster FrameworkofLCSc LiangdongShi YangGao LeiWu LinShang ClusteringwithXCSonComplexStructureDataset AustralasianConferenceonArtificialIntelligence2008 489 499 LiangdongShi YinghuanShi YangGao ClusteringwithXCSandAgglomerativeRuleMerging IDEAL2009 242 250 LiangdongShi YinhuanShi YangGao LinShang YubinYang XCSc ANovelApproachtoClusteringwithExtendedClassifierSystem InternationalJournalofNeuralSystem 21 1 79 93 2011 Semi supervisedLearningTask SupervisedLearningComponentDealwithlabeleddataSimilartoUCS sUpervisedClassifierSystem Semi supervisedLearningComponentProvidelabelstounlabeleddataSelf learningTri trainingSimilarityMeasure Unlabeledbecomelabeled ChiSu YangGao ChunCao Learningclassifiersystemusingbothlabeledandunlabeleddata GECCO2010 1065 1066 Outline IntroductionDefinition HistoryBasicIdeaofLCSTypes ApproachesOurCurrentProgressWhatwehavedone HotIssuesandFutureDirectionWhatisapromisingfutureresearchdirection Complexity AccuracyTradeoff KnowledgerepresentationHighInterpretabilityRule sCreditassignmentHighaccuracyDifficulty Theyareconflicting Complexity AccuracyTradeoff Complexity 0 Error Testdataaccuracy Trainingdataaccuracy Complexity AccuracyTradeoff Complexity 0 Error GoodTradeoff Testdataaccuracy Trainingdataaccuracy FitnessofaSystemS FitnessofaSystemS w1Accuracy S w2Complexity S Thenumberofcorrectlyclassifiedtrainingpatterns Thenumberoffuzzyrules 1stTerm AccuracyMaximization2ndTerm ComplexityMinimization Fitness S Complexity Error Testdataaccuracy S 0 Trainingdataaccuracy Minimizew1 Error w2 ComplexityWhentheweightforthecomplexityminimizationislarge Toosimple DifficultyinWeightedSumApproach Complexity Error Testdataaccuracy S 0 Trainingdataaccuracy Minimizew1 E