Differential prioritization between relevance and redundancy in correlation-based feature selection techniques for multiclass ge

Background: Due to the large number of genes in a typical microarray dataset, feature selection looks set to play an important role in reducing noise and computational cost in gene expressionbased tissue classification while improving accuracy at the same time. Surprisingly, this does not appear to be the case for all multiclass microarray datasets. The reason is that many feature selection techniques applied on microarray datasets are either rank-based and hence do not take into account correlations between genes, or are wrapper-based, which require high computational cost, and often yield difficult-to-reproduce results. In studies where correlations between genes are considered, attempts to establish the merit of the proposed techniques are hampered by evaluation procedures which are less than meticulous, resulting in overly optimistic estimates of accuracy. Results: We present two realistically evaluated correlation-based feature selection techniques which incorporate, in addition ...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Chia Huey Ooi, Madhu Chetty, Shyh Wei Teng
• Keywords: BMCBI 2006, Computational Cost, Microarray Datasets, Multiclass Microarray Datasets