Rough Set Based Learning for Classification
Naohiro Ishii, Takahiro Yamada, Yongguang Bao, H. Tanaka · 2008
The k-nearest neighbor(k-NN) is improved by applying rough set and distance functions with relearning and ensemble computations to classify data with the higher accuracy values. Then, the proposed relearning and combining ensemble computations are an effective technique for improving accuracy. We develop a new approach to combine kNN classifier based on rough set and distance functions with relearning and ensemble computations. The combining algorithm shows higher generalization accuracy, compared to other conventional algorithms. First, to improve classification accuracy, an instance-based learning method with genetic algorithm is developed. Second, additional ensemble computations are followed by the relearning. Then, rough set approach for the classification, is discussed. Experiments have been conducted on some benchmark datasets from the UCI Machine Learning Repository.