International Journal of
Computer Sciences and Engineering

Scholarly, Peer-Reviewed and Fully Refereed Academic Research Journal

Flash News 

Full paper submission has now been opened for August edition. You can upload your full paper using the required templates to the Online Submission System. Deadline for uploading the full papers is 22 August 2018.

Classification Techniques in WEKA: A Review
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.
View this paper at   Google Scholar | DPI Digital Library
  XML View PDF Download  

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.
303 185 downloads 71 downloads
Abstract :
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
References :
[1] E. Hullermeir, “Fuzzy sets in machine learning and data mining”, Elsevier, pp.1493-1505, 2008.
[2] G. Peter Zhang, “Neural Network for Data Mining”, Springer, pp.419-444, 2010.
[3] J. Vashishtha, D. Kumar, S. Ratnoo, “Revisiting Interestingness Measures for Knowledge Discovery in Databases ”, IEEE, pp.72-78, 2012.
[4] K. Lal, N.C. Mahanti, “Role of soft computing as a tool in data mining”, IJCSIT, Vol.2, Issue.1, pp.526-537, 2011.
[5] L. Gagnani, H. Chhinkaniwala, “Soft Computing as a Tool in Data Mining:A Review”, In the Proceedings of the 2015 International Conference on Emerging Trends in Scientific Research (ICETSR 2015), Wadhwan, INDIA, pp.148-155, 2015.
[6] M.F. Otham, T.M. Yau, “Comparison of Different Classification Techniques using WEKA for Breast Cancer”, In the Proceedings of 2007 IFMBE, pp.520-523, 2007.
[7] Marie Fernandes , "Data Mining: A Comparative Study of its Various Techniques and its Process", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.19-23, 2017.
[8] N. Bhargava, S. Dayma, A. Kuar and P. Singh, “An approach for classification using simple CART algorithm in WEKA”, In Proceedings of 2017 ISCO, Coimbatore, INDIA, pp.212-216, 2017.
[9] P. Shabanzadeh and R. Yusof, “An Efficient Optimization method for solving Unsupervised data classification problems”, Computational and Mathematical Methods in Medicine, Hindawi, 9 pages, 2015.
[10] R. Agrawal, T.L Mielinski, A. Swami, “Database Mining:A Performance Perspective”, IEEE Transactions on Knowledge and Data Engineering, Vol. 12, pp.914-925, 1993.
[11] S. Radha Priya and M. Devapriya, "Survey on Attribute Oriented Induction Using Data Mining Techniques", International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.125-129, 2016.
[12] AR. PonPeriasamy, E. Thenmozhi, “A Brief Survey of Data Mining Techniques Applied to Agricultural Data”, International Journal of Computer Sciences and Engineering (IJCSE), Vol. 5, Issue. 4, pp.129-132, 2017.