A Hybrid Method for Fast Finding the Reduct with the Best Classification Accuracy

• Main Authors: KAHRAMANLI, S., ARSLAN, A., HACIBEYOGLU, M.
• Format: Article
• Language: English
• Published: Stefan cel Mare University of Suceava 2013-11-01
• Series: Advances in Electrical and Computer Engineering
• Subjects: artificial intelligence; classification algorithms; decision trees; discernibility function; feature selection
• Online Access: http://dx.doi.org/10.4316/AECE.2013.04010

Usually a dataset has a lot of reducts finding all of which is known to be an NP hard problem. On the other hand, different reducts of a dataset may provide different classification accuracies. Usually, for every dataset, there is only a reduct with the best classification accuracy to obtain this best one, firstly we obtain the group of attributes that are dominant for the given dataset by using the decision tree algorithm. Secondly we complete this group up to reducts by using discernibility function techniques. Finally, we select only one reduct with the best classification accuracy by using data mining classification algorithms. The experimental results for datasets indicate that the classification accuracy is improved by removing the irrelevant features and using the simplified attribute set which is derived from proposed method.