Open AccessAnt System and Weighted Voting Method for Multiple Classifier Systems
Author(s) -
Abdullah Abdullah,
Ku Ruhana Ku-Mahamud
Publication year - 2018
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
ISSN - 2088-8708
DOI - 10.11591/ijece.v8i6.pp4705-4712
Subject(s) - classifier (uml) , random subspace method , computer science , weighted voting , cascading classifiers , majority rule , artificial intelligence , voting , pattern recognition (psychology) , quadratic classifier , machine learning , ensemble learning , data mining , politics , political science , law
Combining multiple classifiers is considered as a general solution for classification tasks. However, there are two problems in combining multiple classifiers: constructing a diverse classifier ensemble; and, constructing an appropriate combiner. In this study, an improved multiple classifier combination scheme is propose. A diverse classifier ensemble is constructed by training them with different feature set partitions. The ant system-based algorithm is used to form the optimal feature set partitions. Weighted voting is used to combine the classifiers’ outputs by considering the strength of the classifiers prior to voting. Experiments were carried out using k-NN ensembles on benchmark datasets from the University of California, Irvine, to evaluate the credibility of the proposed method. Experimental results showed that the proposed method has successfully constructed better k-NN ensembles. Further more the proposed method can be used to develop other multiple classifier systems.