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

  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},
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
3 Citations
14 References
Similar Papers


Publications referenced by this paper.
Showing 1-10 of 14 references

Hewawasam, “A novel belief theoretic association rule mining based classifier for handling class label ambiguities,” in Foundations in Data Mining (FDM) Workshop

  • J. Zhang, S. P. Subasingha, K. Premaratne, M.-L. Shyu, M. Kubat, K K.K.R.G.
  • IEEE International Conference on Data Mining…
  • 2004

UCI repository of machine learning databases,

  • C. L. Blake, C. J. Merz
  • 1998

Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account,

  • I. Bloch
  • Pattern Recognition Letters
  • 1996

Similar Papers

Loading similar papers…