id: 05958679
dt: a
an: 05958679
au: Liu, Kun-Hong; Wu, Qing-Qiang; Wang, Mei-Hong
ti: The design of evolutionary multiple classifier system for the
    classification of microarray data.
so: Liu, Derong (ed.) et al., Advances in neural networks ‒ ISNN 2011. 8th
    international symposium on neural networks, ISNN 2011, Guilin, China,
    May 29 ‒ June 1, 2011. Proceedings, Part III. Berlin: Springer (ISBN
    978-3-642-21110-2/pbk). Lecture Notes in Computer Science 6677, 513-522
    (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: multiple classifier system; genetic algorithm; microarray data
ci: 
li: doi:10.1007/978-3-642-21111-9_58
ab: Summary: 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. In detail,
    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. Then
    we construct an ensemble system with the individuals in last generation
    in two ways: using the nondominated individuals; using all the
    individuals accompanied with a classifier selection process based on
    another GA. We test the two proposed ensemble methods based on two
    microarray data sets, and the experimental results show that these two
    methods are robust and can lead to promising results.
rv: