Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/2489
Title: Memetic algorithms for feature/gene selection
Authors: Zhu, Zexuan
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
Issue Date: 2007
Source: Zhu, Z. X. (2007). Memetic algorithms for feature/gene selection. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: This 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).
URI: https://hdl.handle.net/10356/2489
DOI: 10.32657/10356/2489
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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