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Cancer is a severe condition of uncontrolled cell division that results in a tumor formation that spreads to other tissues of the body. Therefore, the development of new medication and treatment methods for this is in demand. Classification of microarray data plays a vital role in handling such situations. The relevant gene selection is an important step for the classification of microarray data. This work presents gene-encoder, an unsupervised two-stage feature selection technique for the cancer samples' classification. The first stage aggregates three filter methods, namely, Principal Component Analysis (PCA), correlation, and spectral-based feature selection techniques. Next, the Genetic Algorithm (GA) is used, which evaluates the chromosome utilizing the autoencoder-based clustering. The resultant feature subset is used for the classification task. Three classifiers, namely, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Random Forest (RF) are used in this work to avoid the dependency on any one classifier. Six benchmark gene expression datasets are used for the performance evaluation and a comparisons is made with four state-of-the-art related algorithms. Three set of experiments are carried out to evaluate the proposed method. These experiments are for the evaluation of the selected features based on sample-based clustering, adjusting optimal parameters, and for selecting better performing classifier. The comparison is based on accuracy, recall, false-positive rate, precision, Fmeasure, and entropy. The obtained results suggest better performance of the current proposal.

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