Key Gene Selection in Microarray Using Sequential Forward Selection Strategy
| Autor: | Yu-Chao Chen, 陳昱超 |
|---|---|
| Rok vydání: | 2013 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 101 High dimension of feature space、low instance amount、and only a limited number of key genes critical for bioinformation classification problems are three characteristics in the analysis of microarray. On one hand, the selection of discriminative genes is important. On the other hand, a collection of discriminative genes do not necessarily lead to good classification quality. This is because some attributes could likely possess the similar classification effects and in turn lead to the redundant classification results. In order to generate the subsets of genes with not only sufficient but also necessary discrimination power for bioinformation classification problems, a novel selection strategy which integrates fuzzy cluster analyses and information gain (IG) into the traditional sequential forward selection (SFS) algorithm is proposed in this paper. In terms of classification accuracy and discrimination power, the experimental results gained from six microarray datasets show that our strategy can efficiently select compact subsets of characterizing genes and these selected genes are suitable for various conventional classifiers. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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