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

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.