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Titlebook: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing; 11th International C Aijun An,Jerzy Stefanowski,Guoyin Wang Conference proceedi

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樓主: corrode
41#
發(fā)表于 2025-3-28 18:32:27 | 只看該作者
42#
發(fā)表于 2025-3-28 22:12:31 | 只看該作者
Fuzzifying Closure Systems and Fuzzy Latticeslar fuzzy lattice ordered poset. Conversely, every lattice ordered poset is isomorphic to a fuzzifying closure system. In particular, each complete fuzzy lattice is representable by a fuzzifying closure system.
43#
發(fā)表于 2025-3-29 02:59:46 | 只看該作者
Evolution of Fuzzy System Models: An Overview and New Directionsstems using fuzzy set theory. The undeniable merit of FSM is its inherent ability of dealing with uncertain, imprecise, and incomplete data and still being able to make powerful inferences. This paper provides an overview of FSM techniques with an emphasis on new approaches on improving the predicti
44#
發(fā)表于 2025-3-29 04:36:55 | 只看該作者
Retracted Article: A New Cluster Validity Index for Fuzzy Clustering Based on Similarity Measureare review. It is shown that most of these cluster validity indices have serious shortcomings to validate Gustafson Kessel algorithm. Then, a new cluster validity index based on a similarity measure of fuzzy clusters for validation of GK algorithm is presented. This new index is not based on a geome
45#
發(fā)表于 2025-3-29 07:14:38 | 只看該作者
46#
發(fā)表于 2025-3-29 14:51:41 | 只看該作者
47#
發(fā)表于 2025-3-29 18:57:56 | 只看該作者
Nucleus Segmentation and Recognition of Uterine Cervical Pap-Smearsl region is extracted from an image of uterine cervical cytodiagnosis using the region growing method. Segmented images from background and cell areas are binarized using a threshold value. And the 8-directional tracking algorithm for contour lines is applied to extract the cell area. Each extracted
48#
發(fā)表于 2025-3-29 20:46:59 | 只看該作者
49#
發(fā)表于 2025-3-30 03:00:55 | 只看該作者
50#
發(fā)表于 2025-3-30 04:15:21 | 只看該作者
A Rough-Hybrid Approach to Software Defect Classificationthe results include a family-wise 10 fold paired t-test for accuracy and number of rules. The contribution of this paper is the application of a hybrid rough-neuro-fuzzy decision tree method in classifying software defect data.
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