Abstract
Credit card fraudulent transactions are cost-sensitive in nature, where the cost differs in each misclassification transaction. Generally, the classification methods do not work on the cost factor. It considers a constant cost factor for each misclassification. In this paper, it proposes a modified instance-based cost-sensitive decision tree algorithm which reflects on different cost factor for each misclassified transactions. In the proposed algorithm, it implements different instance-based costs into the cost-based impurity measure as well as cost-based pruning approach. Experimentally, it shows that the proposed algorithm performs better in terms of cost savings as compared against classical decision tree algorithms. Additionally, it observes that the smaller trees are generated in minimum computational time.
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References
Iranmehr A, Masnadi-Shirazi H, Vasconcelos N (2019) Cost-sensitive support vector machines. Neurocomputing 343:50–64
Min F, Liu FL, Wen LY, Zhang ZH (2019) Tri-partition cost-sensitive active learning through kNN. Soft Comput 23(5):1557–1572
Elkan C (2004) The foundations of cost-sensitive learning. In: International joint conference on artificial intelligence, vol 17. Lawrence Erlbaum Associates Ltd, pp 973–978
Ngai EW, Hu Y, Wong Y, Chen Y, Sun X (2011) The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis Support Syst 50(3):559–569
Kim J, Choi K, Kim G, Suh Y (2012) Classification cost: an empirical comparison among traditional classifier, cost-sensitive classifier, and metacost. Expert Syst Appl 39(4):4013–4019
Wang T (2013) Efficient techniques for cost-sensitive learning with multiple cost considerations. Ph.D. thesis
Zadrozny B, Langford J, Abe N (2003) Cost-sensitive learning by cost-proportionate example weighting. In: Third IEEE international conference on data mining (ICDM 2003). IEEE, pp 435–442
Yang W, Zhang Y, Ye K, Li L, Xu CZ (2019) FFD: a federated learning based method for credit card fraud detection. In: International conference on big data. Springer, pp 18–32
Bahnsen AC, Aouada D, Ottersten B (2015) Example-dependent cost-sensitive decision trees. Expert Syst Appl 42(19):6609–6619
Bolton RJ, Hand DJ (2002) Statistical fraud detection: a review. Stat Sci 235–249
Hand DJ, Whitrow C, Adams NM, Juszczak P, Weston D (2008) Performance criteria for plastic card fraud detection tools. J Oper Res Soc 59(7):956–962
Bahnsen AC, Stojanovic A, Aouada D, Ottersten B (2013) Cost sensitive credit card fraud detection using bayes minimum risk. In: Proceedings-2013 12th international conference on machine learning and applications (ICMLA 2013), vol 1. IEEE Computer Society, pp 333–338
Bahnsen AC, Aouada D, Ottersten B (2014) Example-dependent cost-sensitive logistic regression for credit scoring. In: 2014 13th international conference on machine learning and applications (ICMLA). IEEE, pp 263–269
Drummond C, Holte RC et al (2003) C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: Workshop on learning from imbalanced datasets II, vol 11. Citeseer, pp 1–8
Trevor H, Robert T, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction
Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Chapman y hall, Wadsworth International Group, Monterey, CA
Draper BA, Brodley CE, Utgoff PE (1994) Goal-directed classification using linear machine decision trees. IEEE Trans Pattern Anal Mach Intell 16(9):888–893
Ling CX, Yang Q, Wang J, Zhang S (2004) Decision trees with minimal costs. In: Proceedings of the twenty-first international conference on machine learning. ACM, p 69
Ting KM (2002) An instance-weighting method to induce cost-sensitive trees. IEEE Trans Knowl Data Eng 14(3):659–665
Vadera S (2010) CSNL: a cost-sensitive non-linear decision tree algorithm. ACM Trans Knowl Discov Data (TKDD) 4(2):6
Pozzolo AD, Caelen O, Johnson RA, Bontempi G (2015) Calibrating probability with undersampling for unbalanced classification. In: 2015 IEEE symposium series on computational intelligence. IEEE, pp 159–166
Van Hulse J, Khoshgoftaar TM, Napolitano A (2007) Experimental perspectives on learning from imbalanced data. In: Proceedings of the 24th international conference on machine learning. ACM, pp 935–942
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Lenka, S.R., Barik, R.K., Patra, S.S., Singh, V.P. (2021). Modified Decision Tree Learning for Cost-Sensitive Credit Card Fraud Detection Model. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds) Advances in Communication and Computational Technology. ICACCT 2019. Lecture Notes in Electrical Engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_113
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DOI: https://doi.org/10.1007/978-981-15-5341-7_113
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