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

Abstract Views: 275  |  PDF Views: 0

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