In the field of medical science, feature (or gene) selection is a burning topic. Microarray data are very important for the feature selection to diagnose any disease. It may be possible that few thousands of features are present in microarray data. That’s the reason why to find out a proper subset of features using conventional algorithms, it becomes an important object. We have to reduce the whole dataset’s dimension in order to produce a proper subset, keeping in mind that we don’t have to miss significant features while removing redundant features. For disease diagnosis small features set with minimal features. Autoencoder technique is proved to be very powerful and efficient for reducing the dimension. Inspired by the Autoencoder technique, both of us built a model based on Folded Autoencoder (FA) for the selection of features set. After that, some deep learning classifiers are applied to check the accuracy of classifier. Performance of Support Vector Machine (SVM) is better than other classifiers after reducing the dimension of features. This model is named as Folded Autoencoder – SVM (FAS). Lastly, there is a comparison of results obtained from whole dataset (without applying FA) and reduced dataset (after applying FA).

PR v1.0, Microarray Data Analysis, Genes, Feature Selection, Folded Auto-encoder, SVM, KNN