Feature selection using self-information uncertainty measures in neighborhood information systems.

The neighborhood rough set model (NRS) has been widely applied to study feature selection. Nevertheless, the dependency, as a significant feature evaluation function in NRS, only focuses on the classification information in the lower approximation and ignores the classification information in the upper approximation, which affects the evaluation effect of this function. Consequently, this paper first defines the fuzziness using the upper approximation and proposes two self-information uncertainty measures based on the dependency and fuzziness. Second, combining the above two self-information uncertainty measures, a more comprehensive approximate self-information is proposed for evaluating the uncertainty of the classification information of feature subsets. Furthermore, a heuristic feature selection algorithm is constructed based on the approximate self-information. Third, to reduce the time cost of the constructed algorithm in processing high-dimensional datasets, we propose a two-stage selection strategy, in which the first stage adopts the Fisher score dimensionality reduction method (FS) with low time cost and stable performance to retain important features in the high-dimensional dataset as a candidate feature subset. Then, the second stage employs our algorithm to further reduce the candidate feature subset. Finally, the results of various feature selection algorithms on eleven datasets are presented, and the comparison results confirm that our algorithm is efficient. [ABSTRACT FROM AUTHOR]