A parallel hill-climbing algorithm to generate a subset of irreducible testors | Applied Intelligence Skip to main content
Springer Nature Link
Log in

A parallel hill-climbing algorithm to generate a subset of irreducible testors

  • Published:
Applied Intelligence Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The generation of irreducible testors from a training matrix is an expensive computational process: all the algorithms reported have exponential complexity. However, for some problems there is no need to generate the entire set of irreducible testors, but only a subset of them. Several approaches have been developed for this purpose, ranging from Univariate Marginal Distribution to Genetic Algorithms. This paper introduces a parallel version of a Hill-Climbing Algorithm useful to find a subset of irreducible testors from a training matrix. This algorithm was selected because it has been one of the fastest algorithms reported in the state-of-the-art on irreducible testors. In order to efficiently store every different irreducible testor found, the algorithm incorporates a digital-search tree. Several experiments with synthetic and real data are presented in this work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Abbasian R, Mouhoub M (2013) A hierarchical parallel genetic approach for the graph coloring problem. Appl Intell 39:510–528

    Article  Google Scholar 

  2. Alba E (2005) Parallel metaheuristics: a new class of algorithms. Wiley series on parallel and distributed computing. Wiley, New York

    Book  Google Scholar 

  3. Bache K, Lichman M (2013) UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. In: School of Information and Computer. University of California Science, Irvine

  4. Bravo A, Ruiz-Shulcloper J (1983) Algorithm MD for the elaboration of the information in pattern recognition problems. Rev Cien Mat (in Spanish) 4(1):123–132

    Google Scholar 

  5. Carrasco-Ochoa J, Martinez-Trinidad J (2004) Feature selection for natural disaster texts classification using testors. In: Proceedings of 5th International Conference on Intelligent Data Engineering and Automated Learning, LCNS 3177. Springer, Berlin Heidelberg New York, pp 424–429

    Google Scholar 

  6. Chegis I, Yablonskii A (1958) Logical methods of control of work of electric schemes. Trudy Mat Inst Steklov (in Russian) 51:270360

    Google Scholar 

  7. Dmitriev A, Zhuravlev I, Krendeliev F (1966) About mathematical principles and phenomena classification. Diskretni Analiz (in Russian) 7:3–15

    Google Scholar 

  8. Diukova E (1976) About an algorithm for constructing test. Sbornik, rabot po matematicheskoi Kibernetiki (in Russian) 1:167–185

    Google Scholar 

  9. Jensen M (2004) Helper-objetives: using multi-objetive evolutionary Algorithms for single-objetive optimization. J Math Model Algoritm 4:323–347

    Article  Google Scholar 

  10. Kohavi R, Jhon G (1997) Wrappers for feature subset selection. Artif Intell 97:273–324

    Article  MATH  Google Scholar 

  11. Lazo-Cortes M, Ruiz-Shulcloper J, Alba-Cabrera E (2001) An Overview of the evolution of the concept of testor. Pattern Recogn 34(4):753–762

    Article  MATH  Google Scholar 

  12. Lopez-Perez S, Lazo-Cortes M, Estrada-Garcia H (1997) Medical electro-diagnostic using pattern recognition tools. In: Proceedings of the iberoamerican workshop on pattern recognition (TIARP 97), pp 237–244

  13. Luke S (2013) Essentials of Metaheuristics (second edition). Lulu (available at http://cs.gmu.edu/sean/book/metaheuristics/)

  14. Mierswa I, Michael W (2006) Information preserving multi-objective feature selection for unsupervised learning. In: Proceedings of the genetic and evolutionary computation conference. ACM Press, pp 1545–1552

  15. Ochoa J, Valdes M, Moctezuma I, Ayala C (2008) Dimension reduction in image databases using the logical combinatorial approach. In: Innovations and advances techniques in systems. Computing sciences and software engineering. Springer, Berlin Heidelberg, pp 260–265

    Google Scholar 

  16. Ortiz-Posadas M, Martinez-Trinidad J, Ruiz-Shulcloper J (2001) A new approach to diferential diagnosis of diseases. Int J Biomed Comput 40(3):179–185

    Article  Google Scholar 

  17. Pons-Porrata A, Ruiz-Shulcloper J, Berlanga-Llavori RA (2003) Method for the automatic summarization of topic-based clusters of documents. In: Proceedings of VIII iberoamerican conference on pattern recognition, LNCS 2905. Springer, Berlin Heidelberg , pp 596–603

    Google Scholar 

  18. Pons-Porrata A, Gil-Garcia R, Berlanga-Llavori R (2007) Using typical testors for feature selection in text categorization. In: Proceedings of XII iberoamerican conference on pattern recognition, LNCS 4756. Springer, Berlin Heidelberg, pp 643–652

    Google Scholar 

  19. Ruiz-Shulcloper J, Abidi M (2002) Logical combinatorial pattern recognition: a review. Recent Research Developments in Pattern Recognition, Transword Research Networks, Kerala, pp 133–176

  20. Ruiz-Shulcloper J, Bravo-Martinez A, Aguila-Feros L (1985) BT and TB algorithms for calculation of all typical testors. Rev Cien Mat (in Spanish) 6(2):11–18

    Google Scholar 

  21. Ruiz-Shulcloper J, Soto A, Fuentes A (1980) A characterization of the typical testor concept in terms of a notable set of columns. Rev Cien Mat (in Spanish) 1(2):123–134

    Google Scholar 

  22. Saeys Y, Degroeve S, Van de Peer Y (2004) Digging into acceptor splice site prediction: an iterative feature selection approach. In: Proceedings of principles and practice of knowledge discovery in databases, pp 386–397

  23. Sanchez-Diaz G, Diaz-Sanchez G, Mora-Gonzalez M, Piza-Davila I, Aguirre-Salado C, Huerta-Cuellar G, Reyes-Cardenas O, Cardenas-Tristan A. (2014) An evolutionary algorithm with acceleration operator to generate a subset of typical testors. Pattern Recogn Lett 41:34–42

    Article  Google Scholar 

  24. Torres D, Torres A, Cuellar F, Torres M, Ponce-de-Leon E, Pinales F. (2014) Evolutionary computation in the identification of Risk Factors, Case of TRALI. Expert Syst Appl 14(18):831–840

    Article  Google Scholar 

  25. Torres D, Torres A, Ponce-de-Leon E (2006) Genetic algorithm and typical testors in feature subset selection problem. In: Proceedings of 6th iberoamerican conference on systemics, cybernetics and informatics, pp 1–5

  26. Valev V, Asaithambi A (2003) On computational complexity of non-reducible descriptors. In: Proceedings of the IEEE International Conference on Information Reuse and Integration, pp 208–211

  27. Valev V, Sankur B (2004) Generalized non-reducible descriptors. Pattern Recognit 37(9):1809–1815

    Article  MATH  Google Scholar 

  28. Valev V, Zhuravlev Y (1991) Integer-valued problems of transforming the training tables in k-valued code in pattern recognition problems. Pattern Recognit 24(4):283–288

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituto Tecnologico y de Estudios Superiores de Occidente, Periferico Sur Manuel Gomez Morin 8585, Tlaquepaque, Jalisco, 45604, Mexico

    Ivan Piza-Davila

  2. Universidad Autonoma de San Luis Potosi, Facultad de Ingenieria, Dr. Manuel Nava 8, San Luis Potosi, SLP, 78290, Mexico

    Guillermo Sanchez-Diaz & Carlos A. Aguirre-Salado

  3. Instituto Nacional de Astrofisica, Optica y Electronica, Luis Enrique Erro no. 1, Tonantzintla, Puebla, 72840, Mexico

    Manuel S. Lazo-Cortes

Authors
  1. Ivan Piza-Davila
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guillermo Sanchez-Diaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carlos A. Aguirre-Salado
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manuel S. Lazo-Cortes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guillermo Sanchez-Diaz.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Piza-Davila, I., Sanchez-Diaz, G., Aguirre-Salado, C.A. et al. A parallel hill-climbing algorithm to generate a subset of irreducible testors. Appl Intell 42, 622–641 (2015). https://doi.org/10.1007/s10489-014-0606-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-014-0606-1

Keywords

Search

Navigation