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

@inproceedings{Hewawasam2004RuleMA,
  title={Rule mining and classification in the presence of feature level and class label ambiguities},
  author={K. K. Rohitha Hewawasam and Kamal Premaratne and M.-L. Shyu and S. P. Subasingha},
  year={2004}
}
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… CONTINUE READING
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