International Journal of Advanced Networking and Applications

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Classification Algorithms with Attribute Selection:An Evaluation Study using WEKA


Affiliations
1 Department of Computer Science, Raja Dorai Singam Govt Arts College, Sivagangai, India
2 Department of Computer Science, Madurai kamaraj University, Madurai, India
3 Department of CA & IT, Thiagarajar College, Madurai, India
4 Department of CA, NIT, Tiruchi, India
 

Attribute or feature selection plays an important role in the process of data mining. In general the dataset contains more number of attributes. But in the process of effective classification not all attributes are relevant. Attribute selection is a technique used to extract the ranking of attributes. Therefore, this paper presents a comparative evaluation study of classification algorithms before and after attribute selection using Waikato Environment for Knowledge Analysis (WEKA). The evaluation study concludes that the performance metrics of the classification algorithm, improves after performing attribute selection. This will reduce the work of processing irrelevant attributes.

Keywords

Attribute Filters, Attribute Selection, Classification, Data Mining, Weka.
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  • Meenatchi V.T, Gnanambal S, et.al, Comparative Study and Analysis of Classification Algorithms through Machine Learning, International Journal of Computer Engineering and Applications, 9(1),247-252,2018.

  • Hany M. Harb1, Malaka A. Moustafa, Selecting optimal subset of features for student performance model, IJCSI , 9(5), 2012, 1694-0814

  • Hwang, Young-Sup,Wrapper-based Feature Selection Using Support Vector Machine, Department of Computer Science and Engineering, Sun Moon University, Asan, Sunmoonro, Korea, Life Science Journal,11 (7), 221-70,2014.

  • Wang Liping, Feature Selection Algorithm Based On Conditional Dynamic Mutual Information, International Journal Of Smart Sensing and Intelligent Systems,8(1), 2015.

  • Qinbao Song, Jingjie Ni and Guangtao Wang, A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data, IEEE Transactions on Knowledge and Data Engineering, 25(1), 2013.

  • Z.Zhao, H.Liu, On Similarity Preserving Feature Selection, IEEE Transactions on Knowledge and Data Engineering, 25(3), 2013.

  • Sunita Beniwal and Jitender Arora, Classification and Feature Selection Techniques in Data Mining, International Journal of Engineering Research & Technology (IJERT), 1(6), 2012.

  • Mital Doshi and Setu K Chaturvedi, Correlation Based Feature Selection (Cfs) Technique To Predict Student Perfromance, International Journal of Computer Networks & Communications (IJCNC),6(3),2014.

  • M. Ramaswami and R. Bhaskaran,A Study on Feature Selection Techniques in Educational Data Mining, Journal Of Computing,1(1),December 2009.

  • K.Sutha and J. Jebamalar Tamilselv, A Review of Feature Selection Algorithms for Data Mining Techniques, International Journal on Computer Science and Engineering (IJCSE), 7(6), ,2015.

  • Gnanambal S and Thangaraj M, A new architectural framework for Rule Based Healthcare System using Semantic Web Technologies, International Journal of Computers in Healthcare, Inderscience, 2(1), 2014, 1-14. ISSN: 1755-3202.


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  • Classification Algorithms with Attribute Selection:An Evaluation Study using WEKA

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Authors

S. Gnanambal
Department of Computer Science, Raja Dorai Singam Govt Arts College, Sivagangai, India
M. Thangaraj
Department of Computer Science, Madurai kamaraj University, Madurai, India
V. T. Meenatchi
Department of CA & IT, Thiagarajar College, Madurai, India
V. Gayathri
Department of CA, NIT, Tiruchi, India

Abstract


Attribute or feature selection plays an important role in the process of data mining. In general the dataset contains more number of attributes. But in the process of effective classification not all attributes are relevant. Attribute selection is a technique used to extract the ranking of attributes. Therefore, this paper presents a comparative evaluation study of classification algorithms before and after attribute selection using Waikato Environment for Knowledge Analysis (WEKA). The evaluation study concludes that the performance metrics of the classification algorithm, improves after performing attribute selection. This will reduce the work of processing irrelevant attributes.

Keywords


Attribute Filters, Attribute Selection, Classification, Data Mining, Weka.

References