Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Abstract

Data mining in the educational field can be used to optimize the teaching and learning performance among the students. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized to mine the data effectively. This study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked SparseAutoencoder (ISOFS-OSSAE) for data mining and pattern recognition in the educational sector. The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process. Moreover, the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of features. Moreover, the swallow swarm optimization (SSO) with the SSAE model is derived to perform the classification process. To showcase the enhanced outcomes of the ISOFSOSSAE model, a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine (UCI) Machine Learning Repository. The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.