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1. A decision rule-based method for feature selection in predictive data mining

Author

Lutu, Patricia E.N. and Engelbrecht, Andries P.

Subjects
*DECISION making, *PREDICTIVE control systems, *DATA mining, *MACHINE learning, *PATTERN perception, *CLASSIFICATION, *ALGORITHMS, *STATISTICAL correlation, *HEURISTIC programming
Abstract

Abstract: Algorithms for feature selection in predictive data mining for classification problems attempt to select those features that are relevant, and are not redundant for the classification task. A relevant feature is defined as one which is highly correlated with the target function. One problem with the definition of feature relevance is that there is no universally accepted definition of what it means for a feature to be ‘highly correlated with the target function or highly correlated with the other features’. A new feature selection algorithm which incorporates domain specific definitions of high, medium and low correlations is proposed in this paper. The proposed algorithm conducts a heuristic search for the most relevant features for the prediction task. [Copyright &y& Elsevier]

Published
2010
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2. An efficient rough feature selection algorithm with a multi-granulation view

Author

Liang, Jiye, Wang, Feng, Dang, Chuangyin, and Qian, Yuhua

Subjects
*SET theory, *ALGORITHMS, *GRANULATION, *PATTERN perception, *MACHINE learning, *DATA mining, *DATA analysis
Abstract

Abstract: Feature selection is a challenging problem in many areas such as pattern recognition, machine learning and data mining. Rough set theory, as a valid soft computing tool to analyze various types of data, has been widely applied to select helpful features (also called attribute reduction). In rough set theory, many feature selection algorithms have been developed in the literatures, however, they are very time-consuming when data sets are in a large scale. To overcome this limitation, we propose in this paper an efficient rough feature selection algorithm for large-scale data sets, which is stimulated from multi-granulation. A sub-table of a data set can be considered as a small granularity. Given a large-scale data set, the algorithm first selects different small granularities and then estimate on each small granularity the reduct of the original data set. Fusing all of the estimates on small granularities together, the algorithm can get an approximate reduct. Because of that the total time spent on computing reducts for sub-tables is much less than that for the original large-scale one, the algorithm yields in a much less amount of time a feature subset (the approximate reduct). According to several decision performance measures, experimental results show that the proposed algorithm is feasible and efficient for large-scale data sets. [Copyright &y& Elsevier]

Published
2012
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