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Monotonicity in Ant Colony Classification Algorithms

  • James BrookhouseEmail author
  • Fernando E. B. Otero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9882)

Abstract

Classification algorithms generally do not use existing domain knowledge during model construction. The creation of models that conflict with existing knowledge can reduce model acceptance, as users have to trust the models they use. Domain knowledge can be integrated into algorithms using semantic constraints to guide model construction. This paper proposes an extension to an existing ACO-based classification rule learner to create lists of monotonic classification rules. The proposed algorithm was compared to a majority classifier and the Ordinal Learning Model (OLM) monotonic learner. Our results show that the proposed algorithm successfully outperformed OLM’s predictive accuracy while still producing monotonic models.

Keywords

Ant colony optimization Semantic constraints Monotonic Data mining Classification rules Sequential covering 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of ComputingUniversity of KentChatham MaritimeUK

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