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Published in:
2018 | OriginalPaper | Chapter
Attribute Reduction Method Using the Combination of Entropy and Fuzzy Entropy
Authors : Rashmi, Udayan Ghose, Rajesh Mehta
Published in: Networking Communication and Data Knowledge Engineering
Publisher: Springer Singapore
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Abstract
The enormous size datasets are being used in various fields such as administration, engineering, management and so on. For information retreival from these datasets more time is being consumed. Fewer attribute datasets takes lesser time for computation, and are more understandable and intelligible. Attribute reduction is a tool for feature selection as it transforms data into knowledge. A new method using the combination of entropy and fuzzy entropy is proposed for removal of redundancy and irrelevant attributes which results in reducing the dataset size. The functioning of the proposed method is examined on standard datasets such as Sonar, Spambase and Tick-tack-Toe. Experimental results performed on various datasets show that proposed method gives significant improvement in attribute reduction. In this work, nearest neighbor classifier is used to examine the classification accuracy on original and reduced dataset.