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. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
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  3. Brankovic, Aida; Piroddi, Luigi: A distributed feature selection scheme with partial information sharing (2019)
  4. Hodashinsky, I. A.; Sarin, K. S.: Feature selection for classification through population random search with memory (2019)
  5. Wang, Yanjiao; Du, Tianlin: An improved squirrel search algorithm for global function optimization (2019)
  6. Agarwalla, P.; Mukhopadhyay, S.: Feature selection using multi-objective optimization technique for supervised cancer classification (2018)
  7. Shahin, Ismail M. A.: Speaker identification in a shouted talking environment based on novel third-order circular suprasegmental hidden Markov models (2016) ioport
  8. Wang, Zhichun; Li, Minqiang; Li, Juanzi: A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure (2015)
  9. Bolón-Canedo, V.; Porto-Díaz, I.; Sánchez-Maroño, N.; Alonso-Betanzos, A.: A framework for cost-based feature selection (2014) ioport
  10. Lin, Thy-Hou; Tsai, Tsung-Lin: Constructing a linear QSAR for some metabolizable drugs by human or pig flavin-containing monooxygenases using some molecular features selected by a genetic algorithm trained SVM (2014)
  11. Trafalis, Theodore B.; Adrianto, Indra; Richman, Michael B.; Lakshmivarahan, S.: Machine-learning classifiers for imbalanced tornado data (2014) ioport
  12. 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
  13. Duarte-Mermoud, M. A.; Beltrán, N. H.; Salah, S. A.: Probabilistic adaptive crossover applied to Chilean wine classification (2013) ioport
  14. Peng, Xinjun; Xu, Dong: A local information-based feature-selection algorithm for data regression (2013) ioport
  15. Pisica, Ioana; Taylor, Gareth; Lipan, Laurentiu: Feature selection filter for classification of power system operating states (2013)
  16. Prasad, Yamuna; Biswas, K. K.: Fuzzy rough based regularization in generalized multiple kernel learning (2013)
  17. Türkşen, Özlem; Vieira, Susana M.; Madeira, José F. A.; Apaydin, Ayşen; Sousa, João M. C.: Comparison of multi-objective algorithms applied to feature selection (2013) ioport
  18. Boubezoul, Abderrahmane; Paris, Sébastien: Application of global optimization methods to model and feature selection (2012)
  19. Liao, Yen-Far; Yau, Dun-Han; Chen, Chieh-Li: Evolutionary algorithm to traveling salesman problems (2012)
  20. Luque, R. M.; Elizondo, D.; López-Rubio, E.; Palomo, E. J.: Feature selection of hand biometrical traits based on computational intelligence techniques (2012)

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