A chi-square-SVM based pedagogical rule extraction method for microarray data analysis

Simple item page
dc.contributor.authorMukhtar Damola Salawu
dc.contributor.authorMicheal Olaolu Arowolo
dc.contributor.authorSulaiman Olaniyi Abdulsalam
dc.contributor.authorIsiaka, Mope Rafiu
dc.contributor.authorBilkisu Jimada-Ojuolape
dc.contributor.authorMudashiru Lateef Olumide
dc.contributor.authorKazeem A. Gbolagade
dc.date.accessioned2026-05-16T19:53:53Z
dc.date.available2026-05-16T19:53:53Z
dc.date.issued2020-06-10
dc.description.abstractSupport 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.
dc.identifier.issn2252-8814
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/7263
dc.language.isoen
dc.publisherInternational Journal of Advances in Applied Sciences (IJAAS)
dc.titleA chi-square-SVM based pedagogical rule extraction method for microarray data analysis
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A chi-square-SVM based pedagogical rule extraction method for microarray data analysis.pdf
Size:
512.08 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Scholarly Publication