cherub
发表于 2025-3-21 17:40:45
书目名称Belief Functions: Theory and Applications影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0183301<br><br> <br><br>书目名称Belief Functions: Theory and Applications影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0183301<br><br> <br><br>书目名称Belief Functions: Theory and Applications网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0183301<br><br> <br><br>书目名称Belief Functions: Theory and Applications网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0183301<br><br> <br><br>书目名称Belief Functions: Theory and Applications被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0183301<br><br> <br><br>书目名称Belief Functions: Theory and Applications被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0183301<br><br> <br><br>书目名称Belief Functions: Theory and Applications年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0183301<br><br> <br><br>书目名称Belief Functions: Theory and Applications年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0183301<br><br> <br><br>书目名称Belief Functions: Theory and Applications读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0183301<br><br> <br><br>书目名称Belief Functions: Theory and Applications读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0183301<br><br> <br><br>
我正派
发表于 2025-3-21 22:26:56
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微粒
发表于 2025-3-22 03:40:05
https://doi.org/10.1007/978-1-349-86189-7a-cluster to the associated new classes using the .-Nearest neighbor technique. Two experiments show that CClu can handle imbalanced datasets with high accuracy, and the errors are reduced by properly modeling imprecision.
gorgeous
发表于 2025-3-22 06:05:17
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overweight
发表于 2025-3-22 10:07:18
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推迟
发表于 2025-3-22 12:53:16
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眉毛
发表于 2025-3-22 18:15:36
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PHIL
发表于 2025-3-23 00:24:43
Unequal Singleton Pair Distance for Evidential Preference Clusteringy and imprecision. However, traditional distances on belief functions do not adapt to some intrinsic properties of preference relations, especially when indifference relation is taken into comparison, therefore may cause inconsistent results in preference-based applications. In order to solve this i
遗弃
发表于 2025-3-23 03:38:26
Transfer Evidential C-Means Clusteringa powerful clustering algorithm developed in the theoretical framework of belief functions. Based on the concept of credal partition, it extends those of hard, fuzzy, and possibilistic clustering algorithms. However, as a clustering algorithm, it can only work well when the data is sufficient and th
heckle
发表于 2025-3-23 09:14:28
https://doi.org/10.1007/978-3-030-88601-1artificial intelligence; combination rules; computer hardware; computer science; computer systems; comput