撤回: K Logeswaran, P Suresh. 利用遗传算法与非策略强化学习同化以适应性校准交叉操作的高效用项集挖掘。 Comput Intell 38: 1596-1615, 2022 (10.1111/coin.12490)

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computational Intelligence Pub Date : 2024-06-18 DOI:10.1111/coin.12677
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引用次数: 0

摘要

上述文章于 2021 年 11 月 14 日在线发表于 Wiley Online Library (wileyonlinelibrary.com),现经主编 Diana Inkpen 和 Wiley Periodicals LLC 协议撤回。这篇文章是作为客座编辑特刊的一部分发表的。文章发表后,我们注意到有两个被指定为本期特邀编辑的人被一个欺诈实体冒充和/或歪曲。出版商调查后发现,包括本期在内的所有文章在编辑处理和同行评审过程中都受到了损害,这不符合期刊的道德标准。因此,决定撤回这篇文章。我们没有发现作者有任何不当行为的证据。撤稿决定已通知作者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Retraction: K Logeswaran, P Suresh. High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation. Comput Intell 38: 1596–1615, 2022 (10.1111/coin.12490)

The above article, published online on 14 November 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest-edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.

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来源期刊
Computational Intelligence
Computational Intelligence 工程技术-计算机:人工智能
CiteScore
6.90
自引率
3.60%
发文量
65
审稿时长
>12 weeks
期刊介绍: This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.
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