A fast classifier-based approach to credit card fraud detection

openThis thesis aims at addressing the problem of anomaly detection in the context of credit card fraud detection with machine learning. Specifically, the goal is to apply a new approach to two-sample testing based on classifiers recently developed for new physic searches in high-energy physics. This strategy allows one to compare batches of incoming data with a control sample of standard transactions in a statistically sound way without prior knowledge of the type of fraudulent activity. The learning algorithm at the basis of this approach is a modern implementation of kernel methods that allows for fast online training and high flexibility. This work is the first attempt to export this method to a real-world use case outside the domain of particle physics.This thesis aims at addressing the problem of anomaly detection in the context of credit card fraud detection with machine learning. Specifically, the goal is to apply a new approach to two-sample testing based on classifiers recently developed for new physic searches in high-energy physics. This strategy allows one to compare batches of incoming data with a control sample of standard transactions in a statistically sound way without prior knowledge of the type of fraudulent activity. The learning algorithm at the basis of this approach is a modern implementation of kernel methods that allows for fast online training and high flexibility. This work is the first attempt to export this method to a real-world use case outside the domain of particle physics

Impact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images

Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of three popular linear dimensionality reduction methods on the performance of three benchmark anomaly detection algorithms. The Principal Component Analysis (PCA), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) as DR methods, act as pre-processing step for AD algorithms. The assessed AD algorithms are Reed-Xiaoli (RX), Kernel-based versions of the RX (Kernel-RX) and Dual Window-Based Eigen Separation Transform (DWEST). The AD methods have been applied to two hyperspectral datasets acquired by both the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Mapper (HyMap) sensors. The evaluation of experiments has been done using Receiver Operation Characteristic (ROC) curve, visual investigation and runtime of the algorithms. Experimental results show that the DR methods can significantly improve the detection performance of the RX method. The detection performance of neither the Kernel-RX method nor the DWEST method changes when using the proposed methods. Moreover, these DR methods increase the runtime of the RX and DWEST significantly and make them suitable to be implemented in real time applications

Similarity Search Over Graphs Using Localized Spectral Analysis

This paper provides a new similarity detection algorithm. Given an input set of multi-dimensional data points, where each data point is assumed to be multi-dimensional, and an additional reference data point for similarity finding, the algorithm uses kernel method that embeds the data points into a low dimensional manifold. Unlike other kernel methods, which consider the entire data for the embedding, our method selects a specific set of kernel eigenvectors. The eigenvectors are chosen to separate between the data points and the reference data point so that similar data points can be easily identified as being distinct from most of the members in the dataset.Comment: Published in SampTA 201