Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

PloS One
Mohammad Nazmul HaquePablo Moscato

Abstract

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia Universi...Continue Reading

References

Oct 20, 1975·Biochimica Et Biophysica Acta·B W Matthews
Jul 28, 2006·Bio Systems·P MoscatoR Berretta
Aug 21, 2012·PloS One·Giuseppe JurmanCesare Furlanello
Oct 19, 2013·Scandinavian Journal of Rheumatology·H ChenF Zhang

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Citations

May 29, 2021·Frontiers in Microbiology·Padhmanand SudhakarSéverine Vermeire
Oct 26, 2021·Cancer Control : Journal of the Moffitt Cancer Center·Alexandros LaiosDiederick De Jong

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Software Mentioned

GA
WEKA
Enterprise
Logistic
Linux AS
Waikato Environment for Knowledge Analysis ( WEKA )
AdaBoostM1
SimpleLogistic
RMoscato
EoC

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