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An Optimization Rough Set Boundary Region based Random Forest Classifier
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 165 - Number 10 |
| Year of Publication: 2017 |
| Authors: Prerna Diwakar, Anand More |
10.5120/ijca2017914024
|
Prerna Diwakar, Anand More . An Optimization Rough Set Boundary Region based Random Forest Classifier. International Journal of Computer Applications. 165, 10 ( May 2017), 39-43. DOI=10.5120/ijca2017914024
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 we want select attributes that are more significant in the given datasets. We proposed a novel hybrid approach combination of Rough Set with Boundary Region and Random Forest algorithm called Rough Set Boundary Region based Random Forest Classifier (RSBRRF Classifier) which is use to deal with uncertainties, vagueness and ambiguity associated with datasets. In this approach, we select significant attributes based on rough set theory with boundary region as an input to random forest classifier for constructing the decision tree is more efficient and scalable approach for classification of various datasets.
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