Rule mining and classification in the presence of feature level and class label ambiguities

K. K.R.G.K. Hewawasam, K. Premaratne, M. L. Shyu, S. P. Subasingha

Research output: Contribution to journalConference article

1 Scopus citations

Abstract

Numerous applications of topical interest call for knowledge discovery and classification from information that may be inaccurate and/or incomplete. For example, in an airport threat classification scenario, data from heterogeneous sensors are used to extract features for classifying potential threats. This requires a training set that utilizes non-traditional information sources (e.g., domain experts) to assign a threat level to each training set instance. Sensor reliability, accuracy, noise, etc., all contribute to feature level ambiguities; conflicting opinions of experts generate class label ambiguities that may however indicate important clues. To accommodate these, a belief theoretic approach is proposed. It utilizes a data structure that facilitates belief/plausibility queries regarding "ambiguous" itemsets. An efficient apriori-like algorithm is then developed to extract frequent such itemsets and to generate corresponding association rules. These are then used to classify an incoming "ambiguous" data instance into a class label (which may be "hard" or "soft"). To test its performance, the proposed algorithm is compared with C4.5 for several databases from the UCI repository and a threat assessment application scenario.

Original languageEnglish (US)
Article number13
Pages (from-to)98-107
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5803
DOIs
StatePublished - Nov 9 2005
EventIntelligent Computing: Theory and Applications III - Orlando, FL, United States
Duration: Mar 28 2005Mar 29 2005

Keywords

  • Association rules
  • Classification
  • Data ambiguities
  • Data mining
  • Dempster-Shafer belief theory
  • Imperfect data
  • Missing data

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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