RFKNN: ROUGH-FUZZY KNN FOR BIG DATA CLASSIFICATION

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Published: Apr 20, 2018
DOI: https://doi.org/10.26483/ijarcs.v9i2.5667
Keywords:
classification, kNN, big data, clustering, fuzzy sets, rough sets

Main Article Content

Mohamed A. Mahfouz
Ph.D., Faculty of Engineering, Alexandria University, Egypt

Abstract

The K-nearest neighbors (kNN) is a lazy-learning method for classification and regression that has been successfully applied to several application domains. It is simple and directly applicable to multi-class problems however it suffers a high complexity in terms of both memory and computations. Several research studies try to scale the kNN method to very large datasets using crisp partitioning. In this paper, we propose to integrate the principles of rough sets and fuzzy sets while conducting a clustering algorithm to separate the whole dataset into several parts, each of which is then conducted kNN classification. The concept of crisp lower bound and fuzzy boundary of a cluster which is applied to the proposed algorithm allows accurate selection of the set of data points to be involved in classifying an unseen data point. The data points to be used are a mix of core and border data points of the clusters created in the training phase. The experimental results on standard datasets show that the proposed kNN classification is more effective than related recent work with a slight increase in classification time.

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Vol. 9 No. 2 (2018): March-April 2018
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Author Biography

Mohamed A. Mahfouz, Ph.D., Faculty of Engineering, Alexandria University, Egypt

Mohammed Mahfouz is a guest assistant professor in computer& communication engineering program, SSP, faculty of engineering, Alexandria University. He received the B.Sc., M.Sc. and PhD degrees in computer and Systems Engineering from the University of Alexandria, Egypt, in 1989 and 1996 and 2009 respectively. He has published several papers in the areas of bioinformatics and machine learning. Also, he is a recognized reviewer for Elsevier and reviewed several papers for other ranked journals.

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