Functional Link Artificial Neural Network for Classification Task in Data Mining
In solving classification task of data mining, the traditional algorithm such as multi-layer perceptron takes longer time to optimize the weight vectors. At the same time, the complexity of the network increases as the number of layers increases. In this study, we have used Functional Link Artificial Neural Networks (FLANN) for the task of classification. In contrast to multiple layer networks, FLANN architecture uses a single layer feed-forward network. Using the functionally expanded features FLANN overcomes the non-linearity nature of problems, which is commonly encountered in single layer networks. The features like simplicity of designing the architecture and low-computational complexity of the networks encourages us to use it in data mining task. An extensive simulation study is presented to demonstrate the effectiveness of the classifier.
Copyright: © 2007 B. B. Misra and S. Dehuri. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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