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Adaptable multi-phase rules over the infrequent class

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Abstract

Decision trees are a classification model that allow rule generation. Depending upon the type of decision tree model, rules may have one to hundreds of conditions and with repeating data attributes over different conditional values causing the rules to be difficult to understand. To achieve more understandable rules, the number of nodes can be minimized to control the depth of the tree and, therefore, the number of conditions in the rules. Further, the study described in this paper seeks to optimize the decision tree for the generation of rules specific to the infrequent class which presents another challenge since the infrequent class may have few instances in the dataset. Rules that are generated using either decision trees or class association mining generally come from the major class of the dataset. These two mining techniques, decision trees and association mining, are utilized together through ensemble learning in an adaptable manner so that they expand and contract to accommodate the characteristics of the dataset. The ensemble learning occurs in phases: a partially generated or minimized decision tree mining phase, and association mining phase, to increase the probability of finding infrequent class rules. The ensemble learning technique developed in this study is found to generate understandable rules with increased coverage and confidence for the infrequent class with balanced or unbalanced datasets.

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Authors and Affiliations

  1. Software Engineering, University of Houston Clear Lake, Houston, TX, USA

    Soma Datta

  2. Computer Science, Texas Tech University, Lubbock, TX, USA

    Susan Mengel

Authors
  1. Soma Datta
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  2. Susan Mengel
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Correspondence to Soma Datta.

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All the authors declare that they have no conflicts of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by S. Deb, T. Hanne, K. C. Wong.

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Datta, S., Mengel, S. Adaptable multi-phase rules over the infrequent class. Soft Comput 22, 6067–6076 (2018). https://doi.org/10.1007/s00500-018-3399-z

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