Abstract
Cost-sensitive decision tree learning is very important and popular in machine learning and data mining community. There are many literatures focusing on misclassification cost and test cost at present. In real world application, however, the issue of time-sensitive should be considered in cost-sensitive learning. In this paper, we regard the cost of time-sensitive in cost-sensitive 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 misclassification cost.
This work is partially supported by Australian large ARC grants (DP0559536 and DP0667060), a China NSF major research Program (60496327), China NSF grant for Distinguished Young Scholars (60625204), China NSF grants (60463003), an Overseas Outstanding Talent Research Program of Chinese Academy of Sciences (06S3011S01), an Overseas-Returning High-level Talent Research Program of China Ministry of Personnel, a Guangxi NSF grant, and an Innovation Project of Guangxi Graduate Education ( 2006106020812M35).
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Zhang, S., Zhu, X., Zhang, J., Zhang, C. (2007). Cost-Time Sensitive Decision Tree with Missing Values. In: Zhang, Z., Siekmann, J. (eds) Knowledge Science, Engineering and Management. KSEM 2007. Lecture Notes in Computer Science(), vol 4798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76719-0_44
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DOI: https://doi.org/10.1007/978-3-540-76719-0_44
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