Elastic Multi-stage Decision Rules for Infrequent Class

Soma Datta, Susan Mengel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Typically, decision trees are used to represent knowledge by rule generation. To have a better understanding of the rules, it is sometimes necessary to minimize the number of nodes by minimizing the depth of the tree. This study optimizes the depth of the tree by minimizing the number of nodes. Rules that are generated using either decision trees or class association mining are from the major class of the dataset. To enable rules to be created for the infrequent class, this study uses an elastic method, Elastic Multi-Stage Decision Methodology (EMSDM), to create rules for the infrequent group. EMSDM is elastic in that it expands and contracts to accommodate the characteristics of the dataset. In addition, the data analysis occurs in stages: clustering, minimizing the depth of the decision tree, and association mining, to increase the ability of EMSDM to find infrequent class rules. EMSDM shows promise to find infrequent class rules with increased accuracy.

Original languageEnglish
Title of host publicationProceedings - 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages110-114
Number of pages5
ISBN (Electronic)9781509036967
DOIs
StatePublished - Oct 2 2017
Event3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016 - Dubai, United Arab Emirates
Duration: Nov 23 2016Nov 25 2016

Publication series

NameProceedings - 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016

Conference

Conference3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016
CountryUnited Arab Emirates
CityDubai
Period11/23/1611/25/16

Keywords

  • association mining
  • decision tree
  • infrequent classes
  • large datasets
  • multi-stage rule generation
  • rare classes
  • recursive partition
  • rule sets

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