文章基本信息

标题：Performance Analysis of Classifiers to Effieciently Predict Genetic Disorders Using Gene Data
本地全文：下载
作者：R Preethi ; G M SuriyaaKumar ; N G Bhuvaneswari Amma 等
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2014
卷号：2
期号：11
出版社：S&S Publications
摘要：In this paper, we study the performance of various classifier models for predicting disease classes usinggenetic microarray data. We analyze the best from among the four classifier methods namely Naïve Bayes, J48, IB1and IBk. Classification is a technique to predict the best classifier. Classification is used to classify the item accordingto the features of the item with respect to the predefined set of classes. Naive Bayes algorithm is based on probabilityand j48 algorithm is based on decision tree. In this paper, we classify the dataset using classes and we found the J48classifier performs better in accurately predicting the disease classes.
关键词：Prediction; Naive Bayes; J48; IB1; IBk.