Services on Demand
Journal
Article
Indicators
- Cited by SciELO
- Access statistics
Related links
- Similars in SciELO
Share
Computación y Sistemas
On-line version ISSN 2007-9737Print version ISSN 1405-5546
Abstract
IQBAL, Umer; GHAZALI, Rozaida; MUSHTAQ, Muhammad Faheem and KANWAL, Afshan. Functional Expansions Based Multilayer Perceptron Neural Network for Classification Task. Comp. y Sist. [online]. 2018, vol.22, n.4, pp.1625-1635. Epub Feb 10, 2021. ISSN 2007-9737. https://doi.org/10.13053/cys-22-4-2602.
Artificial neural network has been proved among the best tools in data mining for classification tasks. Where, Multilayer Perceptron (MLP) is known as benchmarked technique for classification tasks due to common use and easy implementation. Meanwhile, it is fail to make high combination of inputs from lower feature space to higher feature space. In this paper, Shifted Genocchi polynomials and Chebyshev Wavelets functional expansions based Multilayer Perceptron techniques with Levenberg Marquardt back propagation learning are proposed to deal with high dimension problems in classification tasks. Five datasets from UCI repository and KEEL datasets were collected to evaluate the performance in terms of five evaluation measures. T-test was applied to check the significance of the proposed techniques. The comparison results show that the proposed models outperform in terms of accuracy, sensitivity, specificity, precision and f-measure.
Keywords : Data mining; classification; shifted Genocchi polynomials; Chebyshev wavelets; multilayer perceptron.