Credit card fraud detection along with its inherent property of class imbalance is one of the major challenges faced by the financial institutions. Many classifiers are used for the fraud detection of imbalanced data. Imbalanced data withhold the performance of classifiers by setting up the overall accuracy as a performance measure. This makes the decision to be biased towards the majority clas...

This paper deals with the problem of multi-class classification in machine learning. Various techniques have been successfully proposed to solve such problems, with a computation cost often much higher than techniques dedicated to binary classification. To address this problem, we propose a novel formulation for designing multi-class classifiers, with essentially the same computational complexi...

Motivation: Combinations of classifiers have been found useful empirically, yet there is no formal proof of their generalization ability. Our goal is to develop an algorithm to train a sequence of linear classifiers yielding a nonlinear decision surface. We believe that choosing asymmetric regularization parameters for each class can yield a sequence of classifiers that approximates arbitrarily...

The purpose of this paper is to test the hypothesis that simple classifiers are more robust to changing environments than complex ones. We propose a strategy for generating artificial, but realistic domains, which allows us to control the changing environment and test a variety of situations. Our results suggest that evaluating classifiers on such tasks is not straightforward since the changed ...

The majority of current methods in object classification use the one-against-rest training scheme. We argue that when applied to a large number of classes, this strategy is problematic: as the number of classes increases, the negative class becomes a very large and complicated collection of images. The resulting classification problem then becomes extremely unbalanced, and kernel SVM classifier...

This paper deals with inducing classifiers from imbalanced data, where one class (a minority class) is under-represented in comparison to the remaining classes (majority classes). The minority class is usually of primary interest and it is required to recognize its members as accurately as possible. Class imbalance constitutes a difficulty for most algorithms learning classifiers as they are bi...

Dynamic Ensemble Selection (DES) techniques aim to select only the most competent classifiers for the classification of each test sample. The key issue in DES is how to estimate the competence of classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the set of nearest neighbors of the test sample in th...

Concept drift (non-stationarity) and class imbalance are two important challenges for supervised classifiers. “Concept drift” (or non-stationarity) refers to changes in the underlying function being learnt, and class imbalance is a vast difference between the numbers of instances in different classes of data. Class imbalance is an obstacle for the efficiency of most classifiers. Research on cla...

Binarization techniques are widely used to solve multi-class classification problems. These techniques reduce the classification complexity of multi-class classification problems by dividing the original data set into two-class segments or replicas. Then a set of simpler classifiers are learnt from the two-class segments or replicas. The outputs from these classifiers are combined for final cla...