DistAl: An inter-pattern distance-based constructive learning algorithm Multi-layer networks of threshold logic units offer an attractive framework for the design of pattern classification systems. A new constructive neural network learning algorithm (DistAl) based on inter-pattern distance is introduced. DistAl constructs a single hidden layer of hyperspherical threshold neurons. Each neuron is designed to determine a cluster of training patterns belonging to the same class. The weights and thresholds of the hidden neurons are determined directly by comparing the inter-pattern distances of the training patterns. This offers a significant advantage over other constructive learning algorithms that use an iterative (and often time consuming) weight modification strategy to train individual neurons. The individual clusters (represented by the hidden neurons) are combined by a single output layer of threshold neurons. The speed of DistAl makes it a good candidate for datamining and knowledge acquisition from large datasets. The paper presents results of experiments using several artificial and real-world datasets. The results demonstrate that DistAl compares favorably with other learning algorithms for pattern classification

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  1. Shahin, Ismail M.A.: Speaker identification in a shouted talking environment based on novel third-order circular suprasegmental hidden Markov models (2016) ioport
  2. Bolón-Canedo, V.; Porto-Díaz, I.; Sánchez-Maroño, N.; Alonso-Betanzos, A.: A framework for cost-based feature selection (2014) ioport
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  4. Chen, Hao; Jiang, Wen; Li, Canbing; Li, Rui: A heuristic feature selection approach for text categorization by using chaos optimization and genetic algorithm (2013) ioport
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  6. Peng, Xinjun; Xu, Dong: A local information-based feature-selection algorithm for data regression (2013) ioport
  7. Pisica, Ioana; Taylor, Gareth; Lipan, Laurentiu: Feature selection filter for classification of power system operating states (2013)
  8. Prasad, Yamuna; Biswas, K.K.: Fuzzy rough based regularization in generalized multiple kernel learning (2013)
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  10. Boubezoul, Abderrahmane; Paris, Sébastien: Application of global optimization methods to model and feature selection (2012)
  11. Luque, R.M.; Elizondo, D.; López-Rubio, E.; Palomo, E.J.: Feature selection of hand biometrical traits based on computational intelligence techniques (2012)
  12. Toumi, A.; Khenchaf, A.; Hoeltzener, B.: A retrieval system from inverse synthetic aperture radar images: application to radar target recognition (2012) ioport
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  14. Unler, Alper; Murat, Alper; Chinnam, Ratna Babu: $mr^2$PSO: a maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification (2011) ioport
  15. Abd-Alsabour, Nadia: Feature selection for classification using an ant system approach (2010)
  16. Gheyas, Iffat A.; Smith, Leslie S.: Feature subset selection in large dimensionality domains (2010)
  17. Gonçalves, Laercio B.; Leta, Fabiana R.: Macroscopic rock texture image classification using a hierarchical neuro-fuzzy class method (2010)
  18. Li, Yan-Xiong; Kwong, Sam; He, Qian-Hua; He, Jun; Yang, Ji-Chen: Genetic algorithm based simultaneous optimization of feature subsets and hidden Markov model parameters for discrimination between speech and non-speech events (2010) ioport
  19. Unler, Alper; Murat, Alper: A discrete particle swarm optimization method for feature selection in binary classification problems (2010)
  20. Dessì, Nicoletta; Pes, Barbara: An evolutionary method for combining different feature selection criteria in microarray data classification (2009) ioport

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