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 |Pages: 13
DOI: 10.4018/IJAMC.2017070102
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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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The recent studies have been stated that, a supervised fuzzy logic based adaptive resonance theory neural network called as FAM, which was incorporated with GSA for the classification problem (Tan et al., 2015). They have evolved the connection weights of the FAM with GSA. It has also used a hybrid approach for classification problem which is combined chaotic search with GSA and has used them in the training process of SVM. GSA has been used to determine the real valued parameters of SVM (Li et al., 2015). A Hybrid Neural Network and Gravitational Search Algorithm (HNGSA): a fusion model based on neural network and GSA, has been proposed for a solution of Wessinger’s equation (Kumar, 2014; Ghalambaz et al., 2011). Here, the role of GSA is for providing training to a multi-layer feed-forward neural network. It has integrated support vector machine (SVM) with GSA to increase the classification accuracy in binary class problem and to avoid the parameter fixing problem in SVM. They had taken the performance matrices as Standard deviation, Mean and Max.

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