Titlebook: Rough Sets; International Joint JingTao Yao,Hamido Fujita,Fanzhang Li Conference proceedings 2022 The Editor(s) (if applicable) and The Au

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Scikit-Weak: A Python Library for?Weakly Supervised Machine Learningsupervised and imprecise data learning problems, which, despite their importance in real-world settings, cannot be easily managed by existing libraries. We provide a rationale for the development of such a library, then we discuss its design and the currently implemented methods and classes, which encompass several state-of-the-art algorithms.

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Matrix Representations and?Interdependency on?an?,-fuzzy Covering-Based Rough Set matrix representations of lower and upper approximation operators is to make calculation more valid by means of operations on matrices. Furthermore, in accordance with the concept of .-base, we give a necessary and sufficient condition under what two .-fuzzy .-coverings can generate the same lower and upper approximation operations.

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Binary Boundaries and Power Set Space of Graded Rough Sets and Their Correlative ECG (Electrocardiogf GRSs is established to induce homomorphisms regarding the classical power set space. Finally, the binary boundaries in power set space are utilized for ECG dataset analysis, and experimental results demonstrate the effectiveness of theoretical structures and in-depth properties. This study adopts

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Neighborhood Approximate Reducts-Based Ensemble Learning Algorithm and?Its Application in?Software Dnsure the strong generalization performance of the ensemble learner. In order to verify the effectiveness of ELNAR algorithm, we applied ELNAR algorithm to software defect prediction. Experiments on 20 NASA MDP data sets show that ELNAR algorithm can better improve the performance of software defect

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