Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/2489
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dc.contributor.authorZhu, Zexuanen
dc.date.accessioned2008-09-17T09:04:01Zen
dc.date.available2008-09-17T09:04:01Zen
dc.date.copyright2007en
dc.date.issued2007en
dc.identifier.citationZhu, Z. X. (2007). Memetic algorithms for feature/gene selection. Doctoral thesis, Nanyang Technological University, Singapore.en
dc.identifier.urihttps://hdl.handle.net/10356/2489en
dc.description.abstractThis dissertation presents novel memetic frameworks for the hybridization of wrapper and filter feature selection methods on classification problems. The frameworks incorporate filter methods in the traditional genetic algorithm (GA) to improve classification performance and accelerate the search in identifying the core feature subsets. Particularly, the filter methods are introduced to add or delete features from a candidate feature subset encoded in a GA solution. Using memetic frameworks, we propose and systematicall study three feature selection algorithms, Wrapper-Filter Feature Selection Algorithm (WFFSA), Markov Blanket Embedded Genetic Algorithm (MBEGA), and Markov Blanket Embedded Multiobjective Memetic Algorithm (MBE-MOMA).en
dc.rightsNanyang Technological Universityen
dc.subjectDRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciencesen
dc.titleMemetic algorithms for feature/gene selectionen
dc.typeThesisen
dc.contributor.supervisorOng Yew Soonen
dc.contributor.schoolSchool of Computer Engineeringen
dc.description.degreeDOCTOR OF PHILOSOPHY (SCE)en
dc.identifier.doi10.32657/10356/2489en
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