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：2015
卷号：3
期号：10
DOI：10.15680/IJIRCCE.2015.0310036
出版社：S&S Publications
摘要：Classification is a data mining technique which is based on machine learning. Basically classification is used to classify each item in a set of data into one of predefined set of classes or groups. Classification method makes use of mathematical techniques such as decision trees, linear programming, neural network and statistics. Therefore, the key objective of the learning algorithm is to co nstruct models wi th good generality capability. That is the models that accurately predict the class labels of previously unknown records. In this paper we are analyzing the performance of 3 classifiers algorithms namely Na.ve Bayes, Instance Based K -Nearest Neighbor (IBK) and Random Forest (RF). From the experimental results, it is found that Na.ve Bayes algorithm performs better than the other algorithms. For the comparison of different classification algorithms, we used the ecoli protein datasets. The cross validation parameter is used for calculating the performance of the classification algorithms. From the experimental results, it is inferred that the Na.ve Bayes algorithms performs better than the other algorithms
关键词：Classification; Navie Bayes; Instance Based K-Nearest Neighbor (IBK); Random Forest ; Ecoli; Dataset; Cross validation
