Cost-time sensitive decision tree with missing values

Publication Type:
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, 4798 LNAI pp. 447 - 459
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Cost-sensitive decision tree learning is very important and popular in machine learning and data mining community. There are many literatures focusing on misclassiflcation cost and test cost at present. In real world application, however, the issue of time-sensitive should be considered in costsensitive learning. In this paper, we regard the cost of time-sensitive in costsensitive learning as waiting cost (referred to WC), a novelty splitting criterion is proposed for constructing cost-time sensitive (denoted as CTS) decision tree for maximal decrease the intangible cost. And then, a hybrid test strategy that combines the sequential test with the batch test strategies is adopted in CTS learning. Finally, extensive experiments show that our algorithm outperforms the other ones with respect to decrease in misclassiflcation cost. © Springer-Verlag Berlin Heidelberg 2007.
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