Decision Table Reduction based on Conditional Information Entropy

By Wang Guo. 2002

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Attribute reduction in decision-theoretic rough set models (2008, background): “For example, Shannon’s entropy and many of its variations have been explored to measure the uncertainty in rough set theory [3,7,15,19,28,37,39], and thus can be understood as different forms of the f measure.”
Information-preserving hybrid data reduction based on fuzzy-rough techniques (2006, methodology): “Consistency of data (Mi et al., 2004; Pawlak, 1991), dependency of attributes (Wang and Miao, 1998), mutual information (Wang et al., 2002), discernibility matrix (Skowron and Rauszer, 1992) are employed to find reducts of an information system.”
Incomplete Multigranulation Rough Set (2010, methodology): “Rough-set methodology presents a novel paradigm to deal with uncertainty and has been applied to feature selection [18], [48], [49], knowledge reduction [9], [16], [51], rule extraction [1], [8], [35], [50], [59], uncertainty reasoning [20], [25], [31], decision evaluation [36]–[38], and granular co”
Fuzzy probabilistic approximation spaces and their information measures (2006, methodology): “been applied to feature selection [1], [2], knowledge reduction [3], [ 36 ], [38], rule extraction [4]‐[6], uncertainty reasoning [7], [8] and granulation computing [9], [33], [34], [42].”