The increment of computer technology use and the continued growth of companies have
enabled most financial transactions to be performed through the electronic commerce …

Concept drift primarily refers to an online supervised learning scenario when the relation
between the input data and the target variable changes over time. Assuming a general …

Most machine learning models are static, but the world is dynamic, and increasing online
deployment of learned models gives increasing urgency to the development of efficient and …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and
reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over …

This paper presents recurring concept drifts (RCD), a framework that offers an alternative
approach to handle data streams that suffer from recurring concept drifts (on-line learning). It …

Dynamic real-world applications that generate data continuously have introduced new
challenges for the machine learning community, since the concepts to be learned are likely …

We present a novel adaptation technique for search engines to better support information-
seeking activities that include both lookup and exploratory tasks. Building on previous …

Most data stream classification techniques assume that the underlying feature space is
static. However, in real-world applications the set of features and their relevance to the target …

The technological advances in smartphones and their widespread use has resulted in the
big volume and varied types of mobile data which we have today. Location prediction …