Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …

Classifier learning with data-sets that suffer from imbalanced class distributions is a
challenging problem in data mining community. This issue occurs when the number of …

Model learning from class imbalanced training data is a long-standing and significant
challenge for machine learning. In particular, existing deep learning methods consider …

Many years have passed since Baesens et al. published their benchmarking study of
classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S …

Enterprise credit evaluation model is an important tool for bank and enterprise risk
management, but how to construct an effective decision tree (DT) ensemble model for …

Develo** accurate analytical credit scoring models has become a major focus for financial
institutions. For this purpose, numerous classification algorithms have been proposed for …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine
learning based fraudulent transaction detection systems are very effective in real-world …

Imbalanced data is a common problem in classification. This phenomenon is growing in
importance since it appears in most real domains. It has special relevance to highly …

The need for controlling and effectively managing credit risk has led financial institutions to
excel in improving techniques designed for this purpose, resulting in the development of …