Open Access   Article

Classification Techniques in WEKA: A Review

K.H. Wandra1 , L.P. Gagnani2

1 Director, Babaria Institute of Technology, Vadodara, INDIA.
2 Dept. of Computer Engineering, C U Shah University, Wadhwan City, INDIA.

Correspondence should be addressed to:

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-8 , Page no. 49-52, Aug-2017


Online published on Aug 30, 2017

Copyright © K.H. Wandra, L.P. Gagnani . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: K.H. Wandra, L.P. Gagnani, “Classification Techniques in WEKA: A Review”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.49-52, 2017.

MLA Style Citation: K.H. Wandra, L.P. Gagnani "Classification Techniques in WEKA: A Review." International Journal of Computer Sciences and Engineering 5.8 (2017): 49-52.

APA Style Citation: K.H. Wandra, L.P. Gagnani, (2017). Classification Techniques in WEKA: A Review. International Journal of Computer Sciences and Engineering, 5(8), 49-52.

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Due to the Internet Revolution there has been a data explosion in recent decades. This is due to the easy availability of Internet at any place and time. Therefore it has become very important to extract relevant information from these explosion of data. Data Mining is extraction or mining of useful information from large amount of data. This can be done manually, semi-automatic or automatically. With an enormous of data stored in databases and data warehouse there is need for development of powerful tools to get meaningful data. Data Mining has many tasks such as Classification, Clustering, etc but Classification has gained much importance. Classification is to classify the data into groups based on its characteristics. WEKA is widely used data mining tool. Here a comparison of various algorithms available in WEKA for classification tasks is done. The dataset considered is iris and various parameters considered for evaluation include accuracy, kappa statistics, mean absolute error and root mean square error. 10 mostly used algorithms are compared. Accuracy is given in terms of CCI (Correctly Classified Instances) and ICI (Incorrectly Classified Instances).

Key-Words / Index Term

Classification, Weka, Data Mining


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