Open Access

ARTICLE

An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

by Mesfer Al Duhayyim¹, Radwa Marzouk^2,3, Fahd N. Al-Wesabi⁴, Maram Alrajhi⁵, Manar Ahmed Hamza^6,*, Abu Sarwar Zamani⁶

1 Department of Natural and Applied Sciences, College of Community - Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Saudi Arabia
3 Department of Mathematics, Faculty of Science, Cairo University, Giza 12613, Egypt
4 Department of Computer Science, Faculty of Science & Art at Mahayil, King Khalid University, Saudi Arabia
5 Department of Information Systems, Faculty of Science & Art at Mahayil, King Khalid University, Saudi Arabia
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2022, 71(1), 1233-1247. https://doi.org/10.32604/cmc.2022.021652

Received 08 July 2021; Accepted 09 August 2021; Issue published 03 November 2021

Abstract

Recent advancements in computer technologies for data processing, collection, and storage have offered several chances to improve the abilities in production, services, communication, and researches. Data mining (DM) is an interdisciplinary field commonly used to extract useful patterns from the data. At the same time, educational data mining (EDM) is a kind of DM concept, which finds use in educational sector. Recently, artificial intelligence (AI) techniques can be used for mining a large amount of data. At the same time, in DM, the feature selection process becomes necessary to generate subset of features and can be solved by the use of metaheuristic optimization algorithms. With this motivation, this paper presents an improved evolutionary algorithm based feature subsets election with neuro-fuzzy classification (IEAFSS-NFC) for data mining in the education sector. The presented IEAFSS-NFC model involves data pre-processing, feature selection, and classification. Besides, the Chaotic Whale Optimization Algorithm (CWOA) is used for the selection of the highly related feature subsets to accomplish improved classification results. Then, Neuro-Fuzzy Classification (NFC) technique is employed for the classification of education data. The IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI repository. The simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.

---

Keywords

---

Citations