Microarray Data Classification Based on Evolutionary Multiple Classifier System
Year: 2011
Type: article
Abstract: Designing an evolutionary multiple classifier system (MCS) is a relatively new research area. In this paper, we propose a genetic algorithm (GA) based MCS for microarray data classification. We construct a feature poll with different feature selection methods first, and then a multi-objective GA is applied to implement ensemble feature selection process so as to generate a set of classifiers. When this GA stops, a set of base classifiers are generated. Here we use all the nondominated individuals in last generation to build an ensemble system and test the proposed ensemble method and the method that apply a classifier selection process to select proper classifiers from all the individuals in last generation. The experimental results show the proposed ensemble method is roubust and can lead to promising results.
Source: Applied Mechanics and Materials
Authors: Zheng Gang Gu, Kun Hong Liu
Institution: Xiamen University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W2001305532