H. Liu, H. Motoda, and M. Dash, A Monotonic Measure for Optimal Feature Selection, Proc. of ECML-98, pages 101-106, 1998. Home/Search Document Details and Download
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Consistency Based Feature Selection - Dash, Liu, Motoda Self-citation (Liu Motoda Dash) (Correct)
....Measure Different search strategies pose further constraints on a selection criterion. We demonstrate that the consistency measure can be employed in common forms of search without modification. Five different algorithms represent standard search strategies: exhaustive Focus [1] complete ABB [13], heuristic SetCover [6] probabilistic LVF [14] and hybrid of ABB and LVF QBB. We examine their advantages and disadvantages. Focus: exhaustive search: Focus [1] starts with an empty set and carries out breadth first search until it finds a minimal subset that predicts pure classes. With ....
....If U is monotonic, no feasible node is omitted and savings of search time do not sacrifice optimality. As pointed out in [19] the measures used in [16] such as accuracy have disadvantages (e.g. non monotonicity) the authors of [19] proposed the concept of approximate monotonicity. ABB [13] is an automated B B algorithm having its bound as the inconsistency rate of the data when the full set of features is used. It starts with the full set of features S 0 , removes one feature from S l Gamma1 j in turn to generate subsets S l j where l is the current level and j specifies ....
H. Liu, H. Motoda, and M. Dash. A monotonic measure for optimal feature selection. In Proceedings of European Conference on Machine Learning, pages 101--106, 1998.
Information-Theoretic Algorithm for Feature Selection - Last, Kandel, Maimon (Correct)
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H. Liu, H. Motoda, and M. Dash, A Monotonic Measure for Optimal Feature Selection, Proc. of ECML-98, pages 101-106, 1998.
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