Fibonacci Polynomials Based Functional Link Neural Network for Classification Tasks

The artificial neural network has been proved among the best tools in data mining for classification tasks. The concept of obtaining more accurate classifier with less computational complexity has been gaining importance, because of day by day increase in the data. Several numbers of models have been developed for classification problems. This paper is the depiction of higher order neural networks especially Fibonacci Functional Link Neural Network (FFLNN) for data classification. The coefficients of individual terms in Fibonacci polynomials are smaller than those of individual terms in the classical orthogonal polynomials. Additionally, less number of terms make it a preferable classifier regarding functional expansion. These properties lead this FFLNN to produce more accurate higher order neural network with less computational complexity to tackle the classification problems. Four datasets were collected from KEEL and LIBSVM dataset repositories. Computational results were compared with three benchmarked models including Chebyshev Functional Link Neural Network (CFLNN), Chebyshev Multilayer Perceptron (CMLP) and Multilayer Perceptron Neural Network (MLP). A t-test was applied to check the significance of the proposed classifier based on classification performance. The findings showed that the proposed classifier outperformed all benchmarked models in all evaluation measures.