Conventional machine learning studies generally assume close-environment scenarios
where important factors of the learning process hold invariant. With the great success of …

The prevalence of mobile phones, the internet-of-things technology, and networks of
sensors has led to an enormous and ever increasing amount of data that are now more …

Dynamic financial distress prediction (DFDP) is important for improving corporate financial
risk management. However, earlier studies ignore the time weight of samples when …

Learning from data streams is a research area of increasing importance. Nowadays, several
stream learning algorithms have been developed. Most of them learn decision models that …

This paper is a survey of recent work in the field of web usage mining for the benefitof
research on the personalization of Web-based information services. The essence of …