Using Real Coded Genetic Algorithms to Improve Naïve Bayes Classifiers

Autor:	Yu-Feng Shen, 沈瑜豐
Rok vydání:	2015
Druh dokumentu:	學位論文 ; thesis
Popis:	103 Data mining is a process which can find some useful information through big data automatically. Nowadays, how to analyze these data and transform them into useful information has been a big challenge in various fields. Therefore, developing an algorithm of data mining which can be applied to analyze big data has been a fashionable issue in these years. In order to improve the problem of Real Coded Genetic Algorithms (RCGA) which is easily trapped in the local value and the capacity of RCGA, this research combines design of experiment, elite reservation, mutation mechanism of RCGA. By using 20 functions verification, this result reveals that the improved RCGA proposed in the study has better accuracy performance. In the end, this research integrates the improved RCGA and Naive Bayesian Classifier (NBC) to become improved NBC, then applies to classify 12 data sets which UCI provided to validate the effectivity of improved NBC. The results implied that the improved NBC developed in the study has better accuracy and lower false negative rate than traditional NBC.
Databáze:	Networked Digital Library of Theses & Dissertations
Externí odkaz:	http://ndltd.ncl.edu.tw/handle/ectzze Zobrazit plný text záznamu