1. bookVolumen 5 (2014): Heft 2 (June 2014)
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1847-9375
Erstveröffentlichung
19 Sep 2012
Erscheinungsweise
2 Hefte pro Jahr
Sprachen
Englisch
Uneingeschränkter Zugang

Decision Tree Approach to Discovering Fraud in Leasing Agreements

Online veröffentlicht: 10 Sep 2014
Volumen & Heft: Volumen 5 (2014) - Heft 2 (June 2014)
Seitenbereich: 61 - 71
Eingereicht: 21 Sep 2013
Akzeptiert: 28 Mar 2014
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1847-9375
Erstveröffentlichung
19 Sep 2012
Erscheinungsweise
2 Hefte pro Jahr
Sprachen
Englisch

1. Apté, C., Weiss, S. (1997), “Data mining with decision trees and decision rules”, Future Generation Computer Systems, Vol. 13, No. 2-3, pp. 197-210.10.1016/S0167-739X(97)00021-6Search in Google Scholar

2. Bhattacharyya, S., et al. (2011), “Data mining for credit card fraud: A comparative study”, Decision Support Systems, Vol. 50, No. 3, pp. 602-613.10.1016/j.dss.2010.08.008Search in Google Scholar

3. Coussement, K., Van den Bossche, F. A., De Bock, K. W. (2014), “Data accuracy’s impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees”, Journal of Business Research, Vol. 67, No. 1, pp. 2751-2758.10.1016/j.jbusres.2012.09.024Search in Google Scholar

4. Huang, S. Y., Tsaih, R. H., Lin, W. Y. (2012), “Unsupervised neural networks approach for understanding fraudulent financial reporting”, Industrial Management & Data Systems, Vol. 112, No. 2, pp. 224-244.10.1108/02635571211204272Search in Google Scholar

5. Li, X. B. (2005), “A scalable decision tree system and its application in pattern recognition and intrusion detection”, Decision Support Systems, Vol. 41, No. 1, pp.112-130.10.1016/j.dss.2004.06.016Search in Google Scholar

6. McCarty, J. A., Hastak, M. (2007), “Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression”, Journal of Business Research, Vol. 60, No. 6, pp. 656-662.10.1016/j.jbusres.2006.06.015Search in Google Scholar

7. Morais, A. I. (2013), “Why companies choose to lease instead of buy? Insights from academic literature”, Academia Revista Latinoamericana de Administración, Vol. 26, No. 3, pp. 432-446.10.1108/ARLA-07-2013-0091Search in Google Scholar

8. Ngai, E.W.T. et al. (2011), “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature”, Decision Support Systems, Vol. 50, No. 3, pp. 559-569.10.1016/j.dss.2010.08.006Search in Google Scholar

9. Sinha, A.T., Zhao, H. (2008), “Incorporating domain knowledge into data mining classifiers: An application in indirect lending”, Decision Support Systems, Vol. 46, No. 1, pp. 287-299.10.1016/j.dss.2008.06.013Search in Google Scholar

10. Smith, C. W., Wakeman, L. M. (1985), “Determinants of corporate leasing activity”, Journal of Finance, Vol. 40, No. 3, pp. 895-911.10.1111/j.1540-6261.1985.tb05016.xSearch in Google Scholar

11. Tsang, S. et al. (2011), “Decision trees for uncertain data”, Knowledge and Data Engineering, IEEE Transactions on, Vol. 23, No. 1, pp. 64-78.10.1109/TKDE.2009.175Search in Google Scholar

12. Wu, S. X., Banzhaf, W. (2010), “The use of computational intelligence in intrusion detection systems: A review”, Applied Soft Computing, Vol. 10, No. 1, pp. 1-35.10.1016/j.asoc.2009.06.019Search in Google Scholar

Empfohlene Artikel von Trend MD

Planen Sie Ihre Fernkonferenz mit Scienceendo