Details

Autor(en) / Beteiligte

• Hura, Gurdeep Singh
• Singh, Ashutosh Kumar
• Siong Hoe, Lau

Titel

Modified Decision Tree Learning for Cost-Sensitive Credit Card Fraud Detection Model

Ist Teil von

• Advances in Communication and Computational Technology, 2020, Vol.668, p.1479-1493

Ort / Verlag

Singapore: Springer Singapore Pte. Limited

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.