Abstract
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.
Highlights
As a machine learning technique, a supervised learning algorithm is usually evaluated with a dataset which includes training samples and testing samples
Selecting the most relevant feature through finding or ranking all the relevant features of the dataset is generally suboptimal for training a classifier, especially if the features include duplicate information, which is called redundant feature. erefore, a maximum relevance minimum redundancy (Mr2) feature selection framework that can eliminate most irrelevant and redundant features to reduce training samples is proposed for gene expression array analysis [5]
We mainly focus on the development of a single dendritic neuron model (DNM) via the nonlinear information processing ability of synapses [24]
Summary
As a machine learning technique, a supervised learning algorithm is usually evaluated with a dataset which includes training samples and testing samples. E objective of the feature selection is to avoid the curse of dimensionality of the dataset and thereafter to improve the classification performance of the classifiers. It can provide better classification accuracy with lower computation cost, and give an easier understanding of the importance of the feature in the dataset. To reduce the influence of redundancy feature on the dataset and save computation cost, in this paper we propose a hybrid model Mr2DNM by combining Mr2 with DNM. Mr2DNM applies an optimal subset to train and generate learning rules, where the optimal subset is obtained by utilizing Mr2 criteria to search and rank the features of the dataset, and DNM is used to evaluate the subset.
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