Improving the Performance of Artificial Neural Networks via Instance Selection and Feature Dimensionality Reduction - Volume 3 Number 2 (Apr. 2013) - IJMLC
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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(2): 176-189 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.297

Improving the Performance of Artificial Neural Networks via Instance Selection and Feature Dimensionality Reduction

Ali Abroudi, Mohammad Shokouhifar, and Fardad Farokhi
Abstract—This paper presents a hybrid approach with two phases for improving the performance of training artificial neural networks (ANNs) by selection of the most important instances for training, and then reduction the dimensionality of features. The ANNs which are applied in this paper for validation, are included Multi-Layer Perceptron (MLP) and Neuro-Fuzzy Network (NFN). In the first phase, the Modified Fast Condensed Nearest Neighbor (MFCNN) algorithm is used to construct the subset with instances very close to the decision boundary. It leads to achieve the instances more useful for training the network. And in the second phase, an Ant-based approach to the supervised reduction of feature dimensionality is introduced, aims to reduce the complexity, and improve the accuracy of learning the ANN. The main purpose of this method is to enhance the classification performance by improving the quality of the training set. Experimental results illustrated the efficiency of the proposed approach.

Index Terms—Artificial neural networks, instance selection, feature selection, ant colony optimization, EDA, MFCNN.

The authors are with the Electrical Engineering Department, Islamic Azad University of Central Tehran Branch, Tehran, Iran. (e-mail: a.abroudi@ieee.org; m_shokouhifar@ieee.org; f_farokhi@iauctb.ac.ir).

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Cite:Ali Abroudi, Mohammad Shokouhifar, and Fardad Farokhi, "Improving the Performance of Artificial Neural Networks via Instance Selection and Feature Dimensionality Reduction," International Journal of Machine Learning and Computing vol. 3, no. 2, pp. 176-180, 2013.

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