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International Journal of Advances in Applied Sciences
Published by Institute of Advanced Engineering and Science
ISSN : 22528814     EISSN : 27222594     DOI : http://doi.org/10.11591/ijaas
Core Subject : Science, Education, Social,
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics
International Journal of Advances in Applied Sciences (IJAAS) is a peer-reviewed and open access journal dedicated to publish significant research findings in the field of applied and theoretical sciences. The journal is designed to serve researchers, developers, professionals, graduate students and others interested in state-of-the art research activities in applied science areas, which cover topics including: chemistry, physics, materials, nanoscience and nanotechnology, mathematics, statistics, geology and earth sciences.
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Articles 11 Documents
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Search results for , issue "Vol 9, No 2: June 2020" : 11 Documents clear
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.

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