DistAl

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


References in zbMATH (referenced in 100 articles , 1 standard article )

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  1. Staerk, Christian; Kateri, Maria; Ntzoufras, Ioannis: High-dimensional variable selection via low-dimensional adaptive learning (2021)
  2. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  3. Agor, Joseph; Özaltın, Osman Y.: Feature selection for classification models via bilevel optimization (2019)
  4. Brankovic, Aida; Piroddi, Luigi: A distributed feature selection scheme with partial information sharing (2019)
  5. Hodashinsky, I. A.; Sarin, K. S.: Feature selection for classification through population random search with memory (2019)
  6. Khan, Burhan; Hanoun, Samer; Johnstone, Michael; Lim, Chee Peng; Creighton, Douglas; Nahavandi, Saeid: A scalarization-based dominance evolutionary algorithm for many-objective optimization (2019)
  7. Wang, Yanjiao; Du, Tianlin: An improved squirrel search algorithm for global function optimization (2019)
  8. Agarwalla, P.; Mukhopadhyay, S.: Feature selection using multi-objective optimization technique for supervised cancer classification (2018)
  9. Shahin, Ismail M. A.: Speaker identification in a shouted talking environment based on novel third-order circular suprasegmental hidden Markov models (2016) ioport
  10. Wang, Zhichun; Li, Minqiang; Li, Juanzi: A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure (2015)
  11. Bolón-Canedo, V.; Porto-Díaz, I.; Sánchez-Maroño, N.; Alonso-Betanzos, A.: A framework for cost-based feature selection (2014) ioport
  12. 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)
  13. Trafalis, Theodore B.; Adrianto, Indra; Richman, Michael B.; Lakshmivarahan, S.: Machine-learning classifiers for imbalanced tornado data (2014) ioport
  14. 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
  15. Duarte-Mermoud, M. A.; Beltrán, N. H.; Salah, S. A.: Probabilistic adaptive crossover applied to Chilean wine classification (2013) ioport
  16. Peng, Xinjun; Xu, Dong: A local information-based feature-selection algorithm for data regression (2013) ioport
  17. Pisica, Ioana; Taylor, Gareth; Lipan, Laurentiu: Feature selection filter for classification of power system operating states (2013)
  18. Prasad, Yamuna; Biswas, K. K.: Fuzzy rough based regularization in generalized multiple kernel learning (2013)
  19. 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
  20. Boubezoul, Abderrahmane; Paris, Sébastien: Application of global optimization methods to model and feature selection (2012)

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