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Clustering-based hybrid feature selection approach for high dimensional microarray data
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DOI：10.1016/j.chemolab.2021.104305.png)
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Abstract
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The DNA microarrays are used to monitor the expression levels of significant genes. Most of the microarray data are assumed to be high dimensional, redundant, and noisy. This paper proposed a clustering-based hybrid gene selection approach to reduce the high dimensionality and increase the classification accuracy of cancer microarray data. The proposed approach uses the combined method of k-means clustering algorithm and signal-to-noise-ratio ranking method as a primary filtering method to reduce the high dimensionality of the microarray dataset. A cellular learning automaton combined with ant colony optimization is then applied on the reduced dataset as a wrapper method to get the optimized gene subset. The classifiers adopted to evaluate the proposed method are support vector machine, K-nearest neighbor, and Naive Bayes. The experiments showed promising results in gene subset selection and classification.
Keywords:
Microarray data
Clustering
Ant colony optimization
Classification
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