Version Space Completeness for Novel Hypothesis Induction in Biomedical Applications

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Conference Proceeding
Proceedings of the International Joint Conference on Neural Networks, 2018, 2018-July
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© 2018 IEEE. Use of traditional discretization methods caused a heavy loss of hypotheses in the induction of version spaces. We present a new discretization method, named two-point discretization, to construct an interval covering all the positive data points of a variable as purely as possible. We prove that the two-point discretization is a necessary and sufficient con- dition to guarantee the completeness of version spaces (i.e., no loss of hypothesis). A linear complexity algorithm is proposed to implement these theories. The algorithm is also applied to real-world bioinformatics problems to induce significant biomedical hypotheses which have been never discovered by the traditional approaches.
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