Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Group search optimizer: an optimization algorithm inspired by animal searching behavior
IEEE Transactions on Evolutionary Computation
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Constructing classifier models for gene expression datasets using informative features enhances prediction performance of concerned models. Here, we propose a hybrid Group Search based feature selection (GSO-FS) algorithm which can select relevant gene subsets that can optimally predict cancerous tissue samples. Our experimental results show that the GSO-FS algorithm in combination with SVM classifier performs quite well.