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A Mutual Information based Two-phase Memetic Algorithm for Large-scale Fuzzy Cognitive Map Learning
IEEE Transactions on Fuzzy Systems ( IF 11.9 ) Pub Date : 2018-08-01 , DOI: 10.1109/tfuzz.2017.2764445
Xumiao Zou , Jing Liu

Various automatic learning algorithms have been proposed to learn fuzzy cognitive maps (FCMs), but most of them were only applied to learn small-scale FCMs and the learned maps obtained by such methods are usually much denser than the real maps. Learning FCMs requires the learning methods to not only determine the existence of links between concepts but also optimize the edge weights, which is the difficulty for FCM learning methods. Therefore, we propose a mutual information (MI)-based two-phase memetic algorithm (MA) for learning large-scale FCMs, termed as MIMA-FCM. In MIMA-FCM, the first phase is oriented to determine the existence of links between concepts by MI, which can reduce the search space significantly for MA, and then MA is used to optimize the edge weights according to the multiple observed response sequences in the second phase. Experiments on both synthetic and real-life data and the application for the gene regulatory network reconstruction problem demonstrate that the proposed method can not only find the plausible existence of links between concepts, but also optimize the edge weights rapidly. The comparison with existing algorithms shows that MIMA-FCM can learn large-scale FCMs with higher accuracy without expert knowledge.

中文翻译:

一种用于大规模模糊认知地图学习的基于互信息的两阶段模因算法

已经提出了各种自动学习算法来学习模糊认知图(FCM),但其中大多数仅用于学习小规模的 FCM,并且通过这种方法获得的学习图通常比真实图要密集得多。学习 FCM 要求学习方法不仅要确定概念之间是否存在联系,还要优化边缘权重,这是 FCM 学习方法的难点。因此,我们提出了一种基于互信息 (MI) 的两阶段模因算法 (MA),用于学习大规模 FCM,称为 MIMA-FCM。在 MIMA-FCM 中,第一阶段通过 MI 来确定概念之间是否存在联系,这可以显着减少 MA 的搜索空间,然后使用 MA 根据多个观察到的响应序列来优化边权重。第二阶段。对合成和现实数据的实验以及基因调控网络重建问题的应用表明,所提出的方法不仅可以找到概念之间可能存在的联系,而且可以快速优化边缘权重。与现有算法的比较表明,MIMA-FCM 可以在没有专家知识的情况下以更高的精度学习大规模 FCM。
更新日期:2018-08-01
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