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Incorporating feature ranking and evolutionary methods for the classification of high-dimensional DNA microarray gene expression data

Author(s): Mani Abedini | Michael Kirley | Raymond Chiong

Journal: Australasian Medical Journal
ISSN 1836-1935

Volume: 6;
Issue: 5;
Start page: 272;
Date: 2013;
Original page

Keywords: Classification | high-dimensional data | feature ranking | microarray gene expression profiling | extended Classifier | System | XCS | GRD-XCS | guided rule discovery XCS | evolutionary algorithms

AbstractBackgroundDNA microarray gene expression classification poses a challenging task to the machine learning domain. Typically, the dimensionality of gene expression data sets could go from several thousands to over 10,000 genes. A potential solution to this issue is using feature selection to reduce the dimensionality.AimThe aim of this paper is to investigate how we can use feature quality information to improve the precision of microarray gene expression classification tasks. Method We propose two evolutionary machine learning models based on the eXtended Classifier System (XCS) and a typical feature selection methodology. The first one, which we call FS-XCS, uses feature selection for feature reduction purposes. The second model is GRD-XCS, which uses feature ranking to bias the rule discovery process of XCS.ResultsThe results indicate that the use of feature selection / ranking methods is essential for tackling high-dimensional classification tasks, such as microarray gene expression classification. However, the results also suggest that using feature ranking to bias the rule discovery process performs significantly better than using the feature reduction method. In other words, using feature quality information to develop a smarter learning procedure is more efficient than reducing the feature set. ConclusionOur findings have shown that extracting feature quality information can assist the learning process and improve classification accuracy. On the other hand, relying exclusively on the feature quality information might potentially decrease the classification performance (e.g., using feature reduction). Therefore, we recommend a hybrid approach that uses feature quality information to direct the learning process by highlighting the more informative features, but at the same time not restricting the learning process to explore other features.
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