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Proceedings Paper

Rule mining and classification in the presence of feature level and class label ambiguities
Author(s): K.K. Rohitha G.K. Hewawasam; Kamal Premaratne; Mei-Ling Shyu; Shaminda P. Subasingha
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Paper 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.

Paper Details

Date Published: 28 March 2005
PDF: 10 pages
Proc. SPIE 5803, Intelligent Computing: Theory and Applications III, (28 March 2005); doi: 10.1117/12.603993
Show Author Affiliations
K.K. Rohitha G.K. Hewawasam, Univ. of Miami (United States)
Kamal Premaratne, Univ. of Miami (United States)
Mei-Ling Shyu, Univ. of Miami (United States)
Shaminda P. Subasingha, Univ. of Miami (United States)

Published in SPIE Proceedings Vol. 5803:
Intelligent Computing: Theory and Applications III
Kevin L. Priddy, Editor(s)

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