期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 26, 期 2, 页码 294-308出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2012.146
关键词
Dynamic data sets; incremental algorithm; feature selection; rough set theory
类别
资金
- National Natural Science Fund of China [71031006, 61303008]
- National Key Basic Research and Development Program of China(973) [2011CB311805, 2013CB329404]
Many real data increase dynamically in size. This phenomenon occurs in several fields including economics, population studies, and medical research. As an effective and efficient mechanism to deal with such data, incremental technique has been proposed in the literature and attracted much attention, which stimulates the result in this paper. When a group of objects are added to a decision table, we first introduce incremental mechanisms for three representative information entropies and then develop a group incremental rough feature selection algorithm based on information entropy. When multiple objects are added to a decision table, the algorithm aims to find the new feature subset in a much shorter time. Experiments have been carried out on eight UCI data sets and the experimental results show that the algorithm is effective and efficient.
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