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The purpose of the proposed method is to reduce the computational complexity and increase the classification accuracy of the selected feature subsets.

If there is an opposing set of attribute values that do not lead to opposing classification decisions (zero probability), the two attributes are considered independent, otherwise dependent.

A new attribute selection algorithm with Inductive probability is implemented and evaluated through extensive experiments, comparing with related attribute selection algorithms over eight datasets such as Molecular Biology, Connect4, Soybean, Zoo, Ballon, Mushroom, Lenses and Fictional from UCI Machine Learning Repository databases.

American Scientific Publishers - Journal of Computational and Theoretical Nanoscience

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