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. Lee, In Gyu; Yoon, Sang Won; Won, Daehan: A mixed integer linear programming support vector machine for cost-effective group feature selection: branch-cut-and-price approach (2022)
  2. Staerk, Christian; Kateri, Maria; Ntzoufras, Ioannis: High-dimensional variable selection via low-dimensional adaptive learning (2021)
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  4. Alharbi, Abir: A genetic-ELM neural network computational method for diagnosis of the Parkinson disease gait dataset (2020)
  5. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  6. Langari, Shadi; Marvi, Hossein; Zahedi, Morteza: Improving of feature selection in speech emotion recognition based-on hybrid evolutionary algorithms (2020)
  7. Agor, Joseph; Özaltın, Osman Y.: Feature selection for classification models via bilevel optimization (2019)
  8. Brankovic, Aida; Piroddi, Luigi: A distributed feature selection scheme with partial information sharing (2019)
  9. Hodashinsky, I. A.; Sarin, K. S.: Feature selection for classification through population random search with memory (2019)
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  11. Wang, Yanjiao; Du, Tianlin: An improved squirrel search algorithm for global function optimization (2019)
  12. Agarwalla, P.; Mukhopadhyay, S.: Feature selection using multi-objective optimization technique for supervised cancer classification (2018)
  13. Shahin, Ismail M. A.: Speaker identification in a shouted talking environment based on novel third-order circular suprasegmental hidden Markov models (2016) ioport
  14. Wang, Zhichun; Li, Minqiang; Li, Juanzi: A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure (2015)
  15. Bolón-Canedo, V.; Porto-Díaz, I.; Sánchez-Maroño, N.; Alonso-Betanzos, A.: A framework for cost-based feature selection (2014) ioport
  16. 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)
  17. Trafalis, Theodore B.; Adrianto, Indra; Richman, Michael B.; Lakshmivarahan, S.: Machine-learning classifiers for imbalanced tornado data (2014) ioport
  18. 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
  19. Duarte-Mermoud, M. A.; Beltrán, N. H.; Salah, S. A.: Probabilistic adaptive crossover applied to Chilean wine classification (2013) ioport
  20. Peng, Xinjun; Xu, Dong: A local information-based feature-selection algorithm for data regression (2013) ioport

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