Mr2DNM: A Novel Mutual Information-Based Dendritic Neuron Model
Mr2DNM: A Novel Mutual Information-Based Dendritic Neuron Model
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DOI：
10.1155/2019/7362931
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发表时间：
2019-08
影响因子：
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通讯作者：
Xiaoxiao Qian;Yirui Wang;Shuyang Cao;Yuki Todo;Shangce Gao
中科院分区：
文献类型：
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作者：
Xiaoxiao Qian;Yirui Wang;Shuyang Cao;Yuki Todo;Shangce Gao
通过引入神经元的可塑性机制，原始的树突神经元模型（DNM）不仅具有令人鼓舞的准确性，而且具有简单的学习规则，在分类任务中取得了成功。然而，在真实的世界中收集的数据包含大量的冗余，这使得DNM分析数据的过程变得复杂和耗时。本文提出了一种可靠的混合模型，它结合了最大相关最小冗余（Mr2）的特征选择技术与DNM（即Mr2DNM）的分类实际分类问题。应用基于互信息的Mr2来评估和排名给定数据集的最具信息性和区分性的特征。获得的最佳特征子集用于训练和测试DNM，用于对医疗、物理和社会场景中出现的五种不同问题进行分类。实验结果表明，Mr2DNM算法在分类精度和计算效率方面均优于DNM等6种分类算法.
By employing a neuron plasticity mechanism, the original dendritic neuron model (DNM) has been succeeded in the classification tasks with not only an encouraging accuracy but also a simple learning rule. However, the data collected in real world contain a lot of redundancy, which causes the process of analyzing data by DNM become complicated and time-consuming. This paper proposes a reliable hybrid model which combines a maximum relevance minimum redundancy (Mr2) feature selection technique with DNM (namely, Mr2DNM) for classifying the practical classification problems. The mutual information-based Mr2 is applied to evaluate and rank the most informative and discriminative features for the given dataset. The obtained optimal feature subset is used to train and test the DNM for classifying five different problems arisen from medical, physical, and social scenarios. Experimental results suggest that the proposed Mr2DNM outperforms DNM and other six classification algorithms in terms of accuracy and computational efficiency.