Functional Link Artificial Neural Network for Classification Task in Data Mining

Abstract

Abstract: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.

A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN

“…So, it is advisable and also a new research direction to choose a small set of alternative functions, which can map the function to the desired extent with an output of significant improvement. FLNN with a trigonometric basis functions for classification (FLANN), as proposed in [51] is obviously an example. Note that Chebyshev FLNN is also another improvement in this direction, the detailed is discussed in Sect.…”

“…Misra and Dehuri [51] has used a FLANN for classification problem in data mining with a hope to get a compact classifier with less computational complexity and faster learning. Purwar et al [104] has proposed a Chebyshev functional link neural network (SyiFLNN) for system identification of unknown dynamic non-linear discrete time systems.…”

“…In other words, higher-order correlations among input components can be used to construct a higher-order network to perform non-linear mappings using only a single layer of units [7]. All these prompted the field of HONs [49,50] like functional link neural networks (FLNNs) [51][52][53][54], ridge polynomial neural networks (RPNNs) [55][56][57], pisigma neural networks [58][59][60][61][62][63][64][65], radial basis functions [66][67][68][69][70] and so on.…”

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

“…FLNN is much more modest than MLP since it has a single-layer network compared to the MLP whilst able to handle a non-linear separable classification and functions approximation tasks. The FLNN architecture is basically a flat network without any hidden layer which has make the learning algorithm used in the network less complicated [23]. In FLNN, the input vector is extended with a suitably enhanced representation of the input nodes, thereby artificially increasing the dimension of the input space [6,7].…”

“…In most previous researches, the learning algorithm used for training the FLNN is the Backpropagation (BP) [8,16,22,23,[25][26][27]. BP learning is developed by Rumelhart [28] in which the network is provided with examples of the inputs and desired outputs to be computed, and then the error (difference between actual and expected results) will be calculated.…”