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All Journal International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Sulaiman Olaniyi Abdulsalam
Kwara State University
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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A chi-square-SVM based pedagogical rule extraction method for microarray data analysis Mukhtar Damola Salawu; Micheal Olaolu Arowolo; Sulaiman Olaniyi Abdulsalam; Rafiu Mope Isiaka; Bilkisu Jimada-Ojuolape; Mudashiru Lateef Olumide; Kazeem A Gbolagade
International Journal of Advances in Applied Sciences Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (524.367 KB) | DOI: 10.11591/ijaas.v9.i2.pp93-100

Abstract

Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms.
Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms Sulaiman Olaniyi Abdulsalam; Micheal Olaolu Arowolo; Yakub Kayode Saheed; Jesutofunmi Onaope Afolayan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 2: June 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i2.2985

Abstract

Customer churn is a serious problem, which is a critical issue encountered by large businesses and organizations. Due to the direct impact on the company's revenues, particularly in sectors such as the telecommunications as well as the banking, companies are working to promote ways to identify the churn of prospective consumers. Hence it is vital to investigate issues that influence customer churn to yield appropriate measures to diminish churn. The major objective of this work is to advance a model of churn prediction that helps telecom operatives to envisage clients that are most probable to be subjected to churn. The experimental approach for this study uses the machine learning procedures on the telecom churn dataset, using an improved Relief-F feature selection algorithm to pick related features from the huge dataset. To quantify the model's performance, the result of classification uses CART and ANN, the accuracy shows that ANN has a high predictive capacity of 93.88% compared to the 91.60% CART classifier
Co-Authors Bilkisu Jimada-Ojuolape Jesutofunmi Onaope Afolayan Kazeem A Gbolagade Micheal Olaolu Arowolo Micheal Olaolu Arowolo Mudashiru Lateef Olumide Mukhtar Damola Salawu Rafiu Mope Isiaka Yakub Kayode Saheed