Abstract
The information entropy developed by Shannon is an effective measure of uncertainty in data, and the rough set theory is a useful tool of computer applications to deal with vagueness and uncertainty data circumstances. At present, the information entropy has been extensively applied in the rough set theory, and different information entropy models have also been proposed in rough sets. In this paper, based on the existing feature selection method by using a fuzzy rough set-based information entropy, a corresponding fast algorithm is provided to achieve efficient implementation, in which the fuzzy rough set-based information entropy taking as the evaluation measure for selecting features is computed by an improved mechanism with lower complexity. The essence of the acceleration algorithm is to use iterative reduced instances to compute the lambda-conditional entropy. Numerical experiments are further conducted to show the performance of the proposed fast algorithm, and the results demonstrate that the algorithm acquires the same feature subset to its original counterpart, but with significantly less time.
Highlights
Rough set theory [1] presented by Pawlak in 1982 is a useful tool to deal with vagueness and uncertainty information in the field of computer sciences
In view of the effectiveness of information entropy to measure uncertainty in formation, information entropy has been extensively applied in the rough set theory to mine knowledge, which mainly concentrates on constructing rough set-based entropy in different information systems to measure the significance of features or the quality of knowledge granules and on exploring practical applications of rough set-based entropy
Liang et al [41] introduced the incremental mechanisms for three representative information entropies and developed a group incremental entropy-based feature selection algorithm based on the rough set theory with multiple instances being added to a decision system
Summary
Rough set theory [1] presented by Pawlak in 1982 is a useful tool to deal with vagueness and uncertainty information in the field of computer sciences. Chen et al [27] introduced the neighborhood entropy to evaluate the uncertainty of neighborhood information systems. Hu et al [39] introduced a fuzzy entropy to measure the uncertainty in kernel approximation based on fuzzy rough sets, and proposed the feature evaluation index and a feature selection algorithm. Sun et al [40] provided the rough entropy-based uncertainty measures for feature selection in incomplete decision systems. Liang et al [41] introduced the incremental mechanisms for three representative information entropies and developed a group incremental entropy-based feature selection algorithm based on the rough set theory with multiple instances being added to a decision system. Zhang et al [44] presented a feature selection method by using the fuzzy rough set-based information entropy.
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