2026-05-02T07:27:57Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/6175 2021-12-13T04:16:24Z 1582963342780:1596102355557 user-uit

Evaluation of Classification System Using Naïve Bayes Classifier and Feature Selection Algorithms Aung Nway Oo In machine learning, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant for use in model construction. The feature selection approach gives enhanced prediction and reduces the computation time. This paper presents the comparative analysis of Naïve Bayes (NB) classifier with using two feature selection approaches namely Principal Component Analysis (PCA) and Correlation-based Feature Subset Selection (CFS). The experimental results prove that feature selection based Naïve Bayes classifier achieve higher accuracy rate. 2018-07-02 http://hdl.handle.net/20.500.12678/0000006175 https://meral.edu.mm/records/6175