Alomari, Osama Ahmad Suleiman
(2018)
Filter-Wrapper Methods For Gene
Selection In Cancer Classification.
PhD thesis, Universiti Sains Malaysia.
Abstract
In microarray gene expression studies, finding the smallest subset of informative
genes from microarray datasets for clinical diagnosis and accurate cancer classification
is one of the most difficult challenges in machine learning task. Many researchers have
devoted their efforts to address this problem by using a filter method, a wrapper method
or a combination of both approaches. A hybrid method is a hybridisation approach between
filter and wrapper methods. It benefits from the speed of the filter approach
and the accuracy of the wrapper approach. Several hybrid filter-wrapper methods have
been proposed to select informative genes. However, hybrid methods encounter a number
of limitations, which are associated with filter and wrapper approaches. The gene
subset that is produced by filter approaches lacks predictiveness and robustness. The
wrapper approach encounters problems of complex interactions among genes and stagnation
in local optima. To address these drawbacks, this study investigates filter and
wrapper methods to develop effective hybrid methods for gene selection. This study
proposes new hybrid filter-wrapper methods based on Maximum Relevancy Minimum
Redundancy (MRMR) as a filter approach and adapted bat-inspired algorithm (BA) as
a wrapper approach. First, MRMR hybridisation and BA adaptation are investigated
to resolve the gene selection problem. The proposed method is called MRMR-BA.
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