Improving the Accuracy of the Sequential Patterns-Based Classifiers

  • José K. Febrer-HernándezEmail author
  • Raudel Hernández-León
  • José Hernández-Palancar
  • Claudia Feregrino-Uribe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)


In this paper, we propose some improvements to the Sequential Patterns-based Classifiers. First, we introduce a new pruning strategy, using the Netconf as measure of interest, that allows to prune the rules search space for building specific rules with high Netconf. Additionally, a new way for ordering the set of rules based on their sizes and Netconf values, is proposed. The ordering strategy together with the “Best K rules” satisfaction mechanism allow to obtain better accuracy than SVM, J48, NaiveBayes and PART classifiers, over three document collections.


Data mining Supervised classification Sequential patterns 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • José K. Febrer-Hernández
    • 1
    Email author
  • Raudel Hernández-León
    • 1
  • José Hernández-Palancar
    • 1
  • Claudia Feregrino-Uribe
    • 2
  1. 1.Centro de Aplicaciones de Tecnologías de Avanzada (CENATAV)PlayaCuba
  2. 2.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)PueblaMexico

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