In real-life credit scoring applications, the case in which the class of defaulters is under-represented in comparison to the class of non-defaulters is a very common situation, but it has still received little attention. The present paper investigates the suitability and performance of several resampling techniques when applied in conjunction with statistical and artificial intelligence predic...

Temporal data mining aims at finding patterns in historical data. Our work proposes an approach to extract temporal patterns from data to predict the occurrence of target events, such as computer attacks on host networks, or fraudulent transactions in financial institutions. Our problem formulation exhibits two major challenges: 1) we assume events being characterized by categorical features an...

In real-world applications, it has been often observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifier performance, in particular with patterns belonging to the less represented classes. This effect becomes especially significant on instance-based learning due to the use of some dissimilarity measure. We analyz...

Authorship analysis of electronic texts assists digital forensics and anti-terror investigation. Author identification can be seen as a single-label multi-class text categorization problem. Very often, there are extremely few training texts at least for some of the candidate authors or there is a significant variation in the text-length among the available training texts of the candidate author...

DNA microarray technology can measure the activities of tens of thousands of genes simultaneously, which provides an efficient way to diagnose cancer at the molecular level. Although this strategy has attracted significant research attention, most studies neglect an important problem, namely, that most DNA microarray datasets are skewed, which causes traditional learning algorithms to produce i...

Protein secondary structures prediction (PSSP) is considered as a challenging task in bioinformatics. Many approaches have been proposed in last few decades in order to solve this problem. Despite the enhancements achieved, the prediction accuracy still remains limited. Accurate prediction of the secondary structure of proteins is a critical step in deducing tertiary structure of proteins and t...

With the rise of internet facilities, a greater number people have started doing online transactions at an exponential rate in recent years as transaction system has eliminated need going to bank physically for every transaction. However, fraud cases also increased causing loss money consumers. Hence, effective detection is hour which can detect fraudulent automatically real-time. Generally, ge...

In this work, cost-free learning (CFL) formally defined in comparison with cost-sensitive learning (CSL). The primary difference between them is that even in the class imbalance problem, a CFL approach provides optimal classification results without requiring any cost information. In point of fact, several CFL approaches exist in the related studies like sampling and some criteriabased approach...

In this paper, the behavior of Modular and Non-Modular Neural Networks trained with the classical backpropagation algorithm in batch mode and applied to classification problems with Multi-Class imbalance is studied. Three different cost functions are introduced in the training algorithm in order to solve the problem in four different databases. The proposed strategies show an improvement in the...