Open Access

ARTICLE

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

by Manar Ahmed Hamza^1,*, Siwar Ben Haj Hassine², Ibrahim Abunadi³, Fahd N. Al-Wesabi^2,4, Hadeel Alsolai⁵, Anwer Mustafa Hilal¹, Ishfaq Yaseen¹, Abdelwahed Motwakel¹

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia
2 Department of Computer Science, College of Science and Arts at Mahayil, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia
3 Department of Information Systems, Prince Sultan University, Riyadh, 11586, Saudi Arabia
4 Faculty of Computer and IT, Sana'a University, Sana'a, 61101, Yemen
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2022, 72(2), 2581-2596. https://doi.org/10.32604/cmc.2022.024764

Received 30 October 2021; Accepted 05 January 2022; Issue published 29 March 2022

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 Selection with Optimal Stacked Sparse Autoencoder (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-OSSAE model 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 ISOFS-OSSAE 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.

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Keywords

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