Abstract
We present a novel approach to multivariate feature ranking in context of microarray data classification that employs a simple genetic algorithm in conjunction with Random forest feature importance measures. We demonstrate performance of the algorithm by comparing it against three popular feature ranking and selection methods on a colon cancer recurrence prediction problem. In addition, we investigate biological relevance of the selected features, finding functional associations of corresponding genes with cancer.
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Popovic, D., Sifrim, A., Moschopoulos, C., Moreau, Y., De Moor, B. (2013). A Hybrid Approach to Feature Ranking for Microarray Data Classification. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41016-1_26
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DOI: https://doi.org/10.1007/978-3-642-41016-1_26
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