Comparing Various Classification Techniques Through Weka for Ovarian Cancer
Priyanka Khare1, Kavita Burse2, Anjana Pandey3

1Priyanka Khare, M.Tech. Scholar, Department of Computer Science and Engineering, Oriental Institute of Science and Technology, Bhopal (M.P), India.
2Dr. Kavita Burse, Director, Oriental Institute of Science and Technology, Bhopal (M.P), India.
3Dr. Anjana Pandey, Assistant Professor, University Institute of Technology RGPV, Bhopal (M.P), India.

Manuscript received on 15 April 2016 | Revised Manuscript received on 25 April 2016 | Manuscript Published on 30 April 2016 | PP: 20-23 | Volume-5 Issue-4, April 2016 | Retrieval Number: D4471045416/16©BEIESP
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Abstract: In today’s world, enormous amount of data is presented in various fields. This data can provide important and helpful information for making important decisions. Data mining is the method of finding valuable information. There are numerous data mining techniques used for extracting information classification is one of them. Classification is the process of classifying various data according to established criteria. In this paper, various classification algorithms are used for classifying the data set before these relevant features are selected by the process of feature selection. The performance of various classifiers is analyzed on the basis of accuracy and time taken to build the model.
Keywords: Feature Selection, Classification, Weka, Interquartile Range, Navies Bayes, Instance Based Learning (IB1) ,K-Nearest Neighbour (IBK) , K-STAR, Logical Analysis Of Data(LAD) Tree

Scope of the Article: Classification