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A Meta-Heuristic Model for Data Classification Using Target Optimization

A Meta-Heuristic Model for Data Classification Using Target Optimization

Rabindra K. Barik, Rojalina Priyadarshini, Nilamadhab Dash
Copyright: © 2017 |Volume: 8 |Issue: 3 |Pages: 13
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522513230|DOI: 10.4018/IJAMC.2017070102
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MLA

Barik, Rabindra K., et al. "A Meta-Heuristic Model for Data Classification Using Target Optimization." IJAMC vol.8, no.3 2017: pp.24-36. http://doi.org/10.4018/IJAMC.2017070102

APA

Barik, R. K., Priyadarshini, R., & Dash, N. (2017). A Meta-Heuristic Model for Data Classification Using Target Optimization. International Journal of Applied Metaheuristic Computing (IJAMC), 8(3), 24-36. http://doi.org/10.4018/IJAMC.2017070102

Chicago

Barik, Rabindra K., Rojalina Priyadarshini, and Nilamadhab Dash. "A Meta-Heuristic Model for Data Classification Using Target Optimization," International Journal of Applied Metaheuristic Computing (IJAMC) 8, no.3: 24-36. http://doi.org/10.4018/IJAMC.2017070102

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Abstract

The paper contains an extensive experimental study which focuses on a major idea on Target Optimization (TO) prior to the training process of artificial machines. Generally, during training process of an artificial machine, output is computed from two important parameters i.e. input and target. In general practice input is taken from the training data and target is randomly chosen, which may not be relevant to the corresponding training data. Hence, the overall training of the neural network becomes inefficient. The present study tries to put forward TO as an efficient methodology which may be helpful in addressing the said problem. The proposed work tries to implement the concept of TO and compares the outcomes with the conventional classifiers. In this regard, different benchmark data sets are used to compare the effect of TO on data classification by using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) optimization techniques.

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