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

Abstract

Biomedical relation extraction is an important preliminary step for knowledge discovery in the biomedicaldomain. 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 votingmethod 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 amongthe selected classifiers, and the voting method used in the classifier combination. The base classifiers are representedin the form of chromosomes, where each chromosome contains all information on the ensemble it represents: the subsetof 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 subsetof classifiers with diverse abilities while maximizing the strengths of the best classifiers in the classifier ensemble for agiven voting method. Two main contributions of this work are the evolution of the voting bit as part of the GA and thenovel 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 comparableto the state-of-the-art systems, thereby justifying our approach.