A Threshold fuzzy entropy based feature selection method applied in various benchmark datasets using Ant-miner algorithm
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2015, Vol 5, Issue 3
Abstract
Large amount of data have been stored and manipulated using various database technologies. Processing all the attributes for the particular means is the difficult task. To avoid such difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half selection strategy and Neural network for threshold selection strategy. After the features are selected, they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer classification methodologies.The test results depicts that Neural network for threshold selection strategy works well in selecting features and Ant-miner methodology works best in bringing out better accuracy with selected feature than processing with original dataset.The obtained result of this experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer algorithm could be a more suitable method for producing good results with fewer features than the original datasets
Authors and Affiliations
P. Devipriya , P. Divya , R. Srivinitha , J. Joshimadevi , S. Saravanan , R. G. Suresh Kumar
Keywords
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