COMPARISON OF DIFFERENT CLASSIFICATION TECHNIQUES ON PIMA INDIAN DIABETES DATA
The development of data-mining applications such as classification and clustering has been applied to large scale data. In this research, we present comparative study of different classification techniques using three data mining tools named WEKA, TANAGRA and MATLAB. The aim of this paper is to analyze the performance of different classification techniques for a set of large data. The algorithm or classifiers tested are Multilayer Perceptron, Bayes Network, J48graft (c4.5), Fuzzy Lattice Reasoning (FLR), NaiveBayes, JRip (RIPPER), Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference Systems(ANFIS). A fundamental review on the selected technique is presented for introduction purposes. The diabetes data with a total instance of 768 and 9 attributes (8 for input and 1 for output) will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.
Classification Neural network Decision tree Rule based classifier Fuzzy lattice Fuzzy inference system ANFIS
Farhana Afroz Rashedur M. Rahman
Department of Electrical Engineering and Computer Science, North South University, Bashundhara, Dhaka, Bangladesh
国际会议
13th International Conference on Enterprise Information System(第13届企业信息系统国际会议 ICEIS 2011)
北京
英文
2249-2252
2011-06-08（万方平台首次上网日期，不代表论文的发表时间）