Performance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset using SVM.
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Date
2020-08-17
Journal Title
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Publisher
Proceedings 17th European, Mediterranean, and Middle Eastern Conference, EMCIS 2020.Dubai, United Arab Emirates.
Abstract
A significant application of microarray gene expression data is the
classification and prediction of biological models. An essential component of
data analysis is dimension reduction. This study presents a comparison study on
a reduced data using Analysis of Variance (ANOVA) and Recursive Feature
Elimination (RFE) feature selection dimension reduction techniques, and evaluates
the relative performance evaluation of classification procedures of Support
Vector Machine (SVM) classification technique. In this study, an accuracy and
computational performance metrics of the processes were carried out on a
microarray colon cancer dataset for classification, SVM-RFE achieved 93%
compared to ANOVA with 87% accuracy in the classification output result.
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Citation
Abdulsalam, S. O., Mohammed, A. A., Ajao, J. F., Ogundokun, R. O., Babatunde, R. S., Nnodim, C. T. and Arowolo, M.O. (2020)