Chemical disease relation extraction task using genetic algorithm with two novel voting methods for classifier subset selection

Authors : Stanley Chıka ONYE, Nazife DİMİLİLER, Arif AKKELEŞ

Pages : 1179-1196

Doi:10.3906/elk-1906-46

View : 26 | Download : 9

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Biomedical relation extraction is an important preliminary step for knowledge discovery in the biomedical domain. This paper proposes a multiple classifier system MCS for the extraction of chemical-induced disease relations. A genetic algorithm GA is employed to select classifier ensembles from a pool of base classifiers. Moreover, the voting method used for combining the members of each of the ensembles is also selected during evolution in the GA framework. The performances of the MCSs are determined by the algorithms used for selecting the classifiers, the diversity among the selected classifiers, and the voting method used in the classifier combination. The base classifiers are represented in the form of chromosomes, where each chromosome contains all information on the ensemble it represents: the subset of classifiers voting and the voting method. The chromosomes are evolved using a variety of genetic selection, mating, and mutation techniques in order to find an optimal solution. The aim of the proposed system is to select the subset of classifiers with diverse abilities while maximizing the strengths of the best classifiers in the classifier ensemble for a given voting method. Two main contributions of this work are the evolution of the voting bit as part of the GA and the novel approach of using two different decision-making under uncertainty techniques as voting methods. Furthermore, two different selection algorithms and crossover operators are employed as ways of increasing variations during evolution. We validated our proposed method on nine different experimental settings and they produced good results comparable to the state-of-the-art systems, thereby justifying our approach.
Keywords : Multiple classifier systems, genetic algorithm, chemical disease relation, relation extraction, text mining, classifier ensemble