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An Efficient Classification based Fuzzy Rough Set Theory using ID3 Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 154 - Number 1 |
| Year of Publication: 2016 |
| Authors: Suchita Raghuwanshi, Ramratan Ahirwal |
10.5120/ijca2016912025
|
Suchita Raghuwanshi, Ramratan Ahirwal . An Efficient Classification based Fuzzy Rough Set Theory using ID3 Algorithm. International Journal of Computer Applications. 154, 1 ( Nov 2016), 31-34. DOI=10.5120/ijca2016912025
Abstract
Machine learning is a concerned with the design and development of algorithms. Machine learning is a programming approach to computers to achieve optimization. Classification is the prediction approach in data mining techniques. Decision tree algorithm is the most common classifier to build tree because of it is easier to implement and understand. Attribute selection is a concept by which more significant attributes are selected in the given datasets. The proposed novel hybrid approach with combine of fuzzy set, rough set and ID3 algorithm called FuzzyRoughSetID3 classifier which is used to deal with uncertainties, vagueness and ambiguity associated with datasets. In this approach the selected significant attributes based on hybridization of fuzzy set and rough set theory by using fuzzy positive region and degree of dependency and take reduct as an input to ID3 classifier for constructing the decision tree which is more efficient and scalable approach as compare to previous methods such as ID3, FID3.
References
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