Hierarchical Behavior Knowledge Space

The original publication is available at www.springerlink.comInternational audienceIn this paper we present a new method for fusing classiers output for problems with a number of classes M > 2. We extend the well-known Behavior Knowledge Space method with a hierarchical ap- proach of the dierent cells. We propose to add the ranking information of the classiers output for the combination. Each cell can be divided into new sub-spaces in order to solve ambiguities. We show that this method allows a better control of the rejection, without using new classiers for the empty cells. This method has been applied on a set of classi- ers created by bagging. It has been successfully tested on handwritten character recognition allowing better-detailed results. The technique has been compared with other classical combination methods

Credit card fraud detection using a hierarchical behavior-knowledge space model

Data Availability: All relevant benchmark data are within the manuscript, given in references [24], [25], and [26]. Relevant real data records are available from a public repository: https://doi.org/10.6084/m9.figshare.17030138.Copyright: © 2022 Nandi et al. With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.Funding: The author(s) received no specific funding for this work

Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models

In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of 0.98 for fraud and non-fraud classes. Other models like the Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate promising results, propelling the potential of QML classification for financial applications. While they exhibit certain limitations, the insights attained pave the way for future enhancements and optimisation strategies. However, challenges exist, including the need for more efficient Quantum algorithms and larger and more complex datasets. The article provides solutions to overcome current limitations and contributes new insights to the field of Quantum Machine Learning in fraud detection, with important implications for its future development.Comment: 30 pages, 15 figures, and 2 table

Credit card fraud detection using a hierarchical behavior-knowledge space model

With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems