A Reduction Algorithm for Analysis of Linear Neuron

Abstract

Problem statement: The aim of feature selection is to select a feature set that is relevant for a given application. Feature selection is complex and remains an important issue in many domains. In the field of neural networks, feature selection has been used in many applications and their methods have been employed. In this study we present neural network approaches to feature selection. Approach: In this study a reduction algorithm of the features vector dimension was described by eliminating its selected components on the basis of analyzing the results of teaching a neuron, which has a linear activation function of the type. In the presented algorithm, the value of the mean square error, which appears after the reduction, is the criterion, on the basis of which the components of the eliminated vector were selected. The algorithm is based on the analysis of the classifier of balances vector. Results: The results of calculations obtained when analyzing the data describing an example-task of medical diagnosis were presented as an illustration. Results from the experiment indicate that the elimination of components of features vector using the Reduction algorithm did not cause the increase of the value of mean square. Conclusion: Our study showed that, results provide experimental evidences on the effectiveness of the proposed approach for feature selection in the bioinformatics applications.