Journal of Parallel and Distributed Computing
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Detecting credit card fraud by genetic algorithm and scatter search
Expert Systems with Applications: An International Journal
Towards an artificial immune system for online fraud detection
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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We apply Artificial Immune Systems(AIS) [4] for credit card fraud detection and we compare it to other methods such as Neural Nets(NN) [8] and Bayesian Nets(BN) [2], Naive Bayes(NB) and Decision Trees(DT) [13]. Exhaustive search and Genetic Algorithm(GA) [7] are used to select optimized parameters sets, which minimizes the fraud cost for a credit card database provided by a Brazilian card issuer. The specifics of the fraud database are taken into account, such as skewness of data and different costs associated with false positives and negatives. Tests are done with holdout sample sets, and all executions are run using Weka [18], a publicly available software. Our results are consistent with the early result of Maes in [12] which concludes that BN is better than NN, and this occurred in all our evaluated tests. Although NN is widely used in the market today, the evaluated implementation of NN is among the worse methods for our database. In spite of a poor behavior if used with the default parameters set, AIS has the best performance when parameters optimized by GA are used.