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

K.K. Rohitha G.K. Hewawasam; Kamal Premaratne; Mei-Ling Shyu; Shaminda P. Subasingha

doi:10.1117/12.603993

28 March 2005 Rule mining and classification in the presence of feature level and class label ambiguities

K.K. Rohitha G.K. Hewawasam, Kamal Premaratne, Mei-Ling Shyu, Shaminda P. Subasingha

Proceedings Volume 5803, Intelligent Computing: Theory and Applications III; (2005) https://doi.org/10.1117/12.603993
Event: Defense and Security, 2005, Orlando, Florida, United States

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.

Citation Download Citation

K.K. Rohitha G.K. Hewawasam, Kamal Premaratne, Mei-Ling Shyu, and Shaminda P. Subasingha "Rule mining and classification in the presence of feature level and class label ambiguities", Proc. SPIE 5803, Intelligent Computing: Theory and Applications III, (28 March 2005); https://doi.org/10.1117/12.603993

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