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From Association to Classification: Inference Using Weight of Evidence
May/June 2003 (vol. 15 no. 3)
pp. 764-767

Abstract—Association and classification are two important tasks in data mining and knowledge discovery. Intensive studies have been carried out in both areas. But, how to apply discovered event associations to classification is still seldom found in current publications. Trying to bridge this gap, this paper extends our previous paper on significant event association discovery to classification. We propose to use weight of evidence to evaluate the evidence of a significant event association in support of, or against, a certain class membership. Traditional weight of evidence in information theory is extended here to measure the event associations of different orders with respect to a certain class. After the discovery of significant event associations inherent in a data set, it is easy and efficient to apply the weight of evidence measure for classifying an observation according to any attribute. With this approach, we achieve flexible prediction.

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Index Terms:
Classification, data mining, event association, pattern discovery, weight of evidence.
Citation:
Yang Wang, Andrew K.C. Wong, "From Association to Classification: Inference Using Weight of Evidence," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 3, pp. 764-767, May-June 2003, doi:10.1109/TKDE.2003.1198405
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