HUSH
发表于 2025-3-23 10:31:08
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枯萎将要
发表于 2025-3-23 17:03:44
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Individual
发表于 2025-3-23 20:25:41
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GLADE
发表于 2025-3-24 01:27:45
A Naive Bayes Classifier Based on Neighborhood Granulationon of independence between features affects its classification accuracy. To solve this problem, this paper studies the theory of granular computing and proposes a naive Bayes classifier based on neighborhood granulation. The neighborhood discriminant function is introduced to perform single-feature
remission
发表于 2025-3-24 05:14:51
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
Devastate
发表于 2025-3-24 08:52:11
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豪华
发表于 2025-3-24 14:04:02
Rule Acquisition in Generalized One-Sided Decision Systemsrmined by ordered attributes. In this paper, we propose methods of mining decision rules based on situations where only condition attributes are ordered (generalized one-sided formal decision context) and both the condition and decision attributes are ordered (generalized one-sided ordered formal de
INERT
发表于 2025-3-24 15:14:43
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Obloquy
发表于 2025-3-24 21:39:15
Multi-label Feature Extraction With Distance-Based Graph Attention Networkta usually consider label correlations, while rarely consider sample correlations. In this paper, we propose a multi-label feature extraction with the distance-based graph attention network (DBGAT) algorithm. First, to easily extract the neighbors of the sample later, we construct an adjacency matri
图表证明
发表于 2025-3-25 02:23:39
Three-way Decision, Three-World Conception, and Explainable AIr is to review and re-interpret various three-world conceptions through the lens of three-way decision. Three-world conceptions offer more insights into three-way decision with new viewpoints, methods, and modes. They can be used to construct easy-to-understand explanations in explainable artificial intelligence (XAI).