Journal
FUZZY SETS AND SYSTEMS
Volume 258, Issue -, Pages 61-78Publisher
ELSEVIER
DOI: 10.1016/j.fss.2014.04.029
Keywords
Rough sets; Fuzzy rough sets; Feature selection; Forward approximation; Accelerator; Granular computing
Funding
- National Natural Science Foundation of China [61322211, 71031006, 61202018, 61303008]
- Program for New Century Excellent Talents in University [NCET-12-1031]
- National Key Basic Research and Development Program of China (973) [2013CB329404, 2013CB329502]
- Research Fund for the Doctoral Program of Higher Education [20121401110013]
- MOE Project of Humanities and Social Sciences [12YJC630174]
- Program for the Innovative Talents of Higher Learning Institutions of Shanxi, China [20120301]
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Fuzzy rough set method provides an effective approach to data mining and knowledge discovery from hybrid data including categorical values and numerical values. However, its time-consumption is very intolerable to analyze data sets with large scale and high dimensionality. Many heuristic fuzzy-rough feature selection algorithms have been developed however, quite often, these methods are still computationally time-consuming. For further improvement, we propose an accelerator, called forward approximation, which combines sample reduction and dimensionality reduction together. The strategy can be used to accelerate a heuristic process of fuzzy-rough feature selection. Based on the proposed accelerator, an improved algorithm is designed. Through the use of the accelerator, three representative heuristic fuzzy-rough feature selection algorithms have been enhanced. Experiments show that these modified algorithms are much faster than their original counterparts. It is worth noting that the performance of the modified algorithms becomes more visible when dealing with larger data sets. (C) 2014 Elsevier B.V. All rights reserved.
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