Performance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset Using SVM

Autor:	S. O. Abdulsalam, Ronke Seyi Babatunde, Chiebuka T. Nnodim, Abubakar Adamu Mohammed, Roseline Oluwaseun Ogundokun, Jumoke Falilat Ajao, Micheal Olaolu Arowolo
Rok vydání:	2020
Předmět:	Support vector machine ComputingMethodologies_PATTERNRECOGNITION Microarray gene expression Computer science business.industry Dimensionality reduction Feature (machine learning) Comparison study Pattern recognition Feature selection Artificial intelligence Analysis of variance business
Zdroj:	Information Systems ISBN: 9783030633950 EMCIS
DOI:	10.1007/978-3-030-63396-7_32
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2027a45295d63e58b9d0e4db37927bd3 https://doi.org/10.1007/978-3-030-63396-7_32 Zobrazit plný text záznamu