Abstract
Live Logic is an integrated approach for support of the learning and decision making in conditions of uncertainty. The approach covers both induction of probabilistic logical hypotheses from known examples and deduction of the plausible solution for an unknown case based on the inducted hypotheses.
The induction method generalizes empirical data, discovering statistical patterns, expressed in logical language. The deduction method uses multidimensional ranking to reconcile contradictory patterns exhibited by a particular case.
The method was applied on clinical data of the patients with prostate cancer who underwent prostatectomy. The goal was to predict biochemical failure based on the pre- and post- operative status of the patient. The patterns found by the method proved to be insightful from the pathologist’s point of view. Most of them had been confirmed on the control dataset.
In our experiments, the predictive accuracy of the Live LogicTM was also higher than that of other tested methods.
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Sapir, M., Verbel, D., Kotsianti, A., Saidi, O. (2005). Live LogicTM: Method for Approximate Knowledge Discovery and Decision Making. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_55
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DOI: https://doi.org/10.1007/11548669_55
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