Sciweavers

Share
ISNN
2007
Springer

Memetic Algorithms for Feature Selection on Microarray Data

10 years 8 months ago
Memetic Algorithms for Feature Selection on Microarray Data
In this paper, we present two novel memetic algorithms (MAs) for gene selection. Both are synergies of Genetic Algorithm (wrapper methods) and local search methods (filter methods) under a memetic framework. In particular, the first MA is a Wrapper-Filter Feature Selection Algorithm (WFFSA) fine-tunes the population of genetic algorithm (GA) solutions by adding or deleting features based on univariate feature filter ranking method. The second MA approach, Markov BlanketEmbedded Genetic Algorithm (MBEGA), fine-tunes the population of solutions by adding relevant features, removing redundant and/or irrelevant features using Markov blanket. Our empirical studies on synthetic and real world microarray dataset suggest that both memetic approaches select more suitable gene subset than the basic GA and at the same time outperforms GA in terms of classification predictions. While the classification accuracies between WFFSA and MBEGA are not significantly statistically different on mos...
Zexuan Zhu, Yew-Soon Ong
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ISNN
Authors Zexuan Zhu, Yew-Soon Ong
Comments (0)
books