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@article{142548,
        author = {Dnyaneshwar Kudande},
        title = {An Ensemble of Classifiers using Dynamic Method on Ambiguous Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {2},
        number = {3},
        pages = {88-95},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=142548},
        abstract = {Theaim ofproposed work istoanalyzethe Instance Selection Algorithmfirst. ThereareWeighted Instance Selection algorithms areavailablesuch aswDROP3 (weighted DecrementalReduction Optimization Procedure 3), wRNN(weighted Reduced NearestNeighbor),which reduces theSamplesetapplied.Then themulticlassInstance Selection isuseful techniqueforreducingspace and timecomplexity. This removes irrelevant, noisy,superfluous instances from TrainingSet.Then themulticlassproblem issolvedby consideringnumberoftwo classproblem thusdesigning multiple  two   class  classifiers  and   its  combined  output produces the resultfor it.The Boostingisuse for providing weightforeach instanceoftrainingset.TheDesigningof ensembleofclassifiersistocombineallclassifiersandlearn byreduced training set. There aredifferenttechniquesare available  for  designing  an  ensemble  such   as   Bagging (Bootstrap Aggregating), Boosting(ADABOOST)and Error CorrectingOutputCode (ECOC) etc.Theoutputof ensemble isbetter than theindividualclassifiers.Theapproach istested with fewbenchmark datasets.Itis found thatClassification accuracyinthecaseofwDROP3algorithm liesbetween70% to87%,butincaseofwRNNalgorithmliesbetween61%to89%andtheGeneralizationaccuracyinthecaseofwDROP3 algorithmliesbetween79%to96%,butinwRNNalgorithmit lies between 75% to94%. Another observation, whenincreasesnumberofClassifiersperEnsemblethenaccuracy improves by0.5to1.5%.},
        keywords = {Datamining, data reduction,instance selection, Classification, weightedinstance selection,ReducedNearest Neighbor.},
        month = {},
        }