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Example-dependent cost-sensitive decision trees
Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust.
Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust. (Signal Processing)
2015 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 42, no 19, p. 6609-6619Article in journal (Refereed) Published
Abstract [en]

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. State-of-the-art example-dependent cost-sensitive techniques only introduce the cost to the algorithm, either before or after training, therefore, leaving opportunities to investigate the potential impact of algorithms that take into account the real financial example-dependent costs during an algorithm training. In this paper, we propose an example-dependent cost-sensitive decision tree algorithm, by incorporating the different example-dependent costs into a new cost-based impurity measure and a new cost-based pruning criteria. Then, using three different databases, from three real-world applications: credit card fraud detection, credit scoring and direct marketing, we evaluate the proposed method. The results show that the proposed algorithm is the best performing method for all databases. Furthermore, when compared against a standard decision tree, our method builds significantly smaller trees in only a fifth of the time, while having a superior performance measured by cost savings, leading to a method that not only has more business-oriented results, but also a method that creates simpler models that are easier to analyze. (C) 2015 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
2015. Vol. 42, no 19, p. 6609-6619
Keywords [en]
Cost-sensitive learning; Cost-sensitive classifier; Credit scoring; Fraud detection; Direct marketing; Decision tree
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-259157DOI: 10.1016/j.eswa.2015.04.042ISI: 000356735100011Scopus ID: 2-s2.0-84930182212OAI: oai:DiVA.org:kth-259157DiVA, id: diva2:1350598
Note

QC20191004

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-10-04Bibliographically approved

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Publisher's full textScopushttp://www.sciencedirect.com/science/article/pii/S0957417415002845

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