In the process of concept learning, target concepts may have portions with short-term
changes, other portions may support long-term changes, and yet others may not change at …

A new algorithm for incremental construction of binary regression trees is presented. This
algorithm, called SAIRT, adapts the induced model when facing data streams involving …

In a large number of problems the data comes from dynamic environments and are acquired
over time, which requires processing sequence. Often, changes occur that cause the model …

This paper applies the Behavior Pattern Learning (BPL) approach to automatically discover
the conditions previous to specific macroeconomic events from the historical evolution of …

Esta tesis se enfoca en el desarrollo de algoritmos de aprendizaje para la minería de flujos
de datos cuya función objetivo puede cambiar en el tiempo. En estos escenarios los datos …

This paper presents a new learning algorithm for inducing decision trees from data streams.
In these domains, large amounts of data are constantly arriving over time, possibly at high …

Actualmente el volumen de datos que se genera en diferentes ámbitos es muy elevado,
llegando incluso a ser difícil de almacenar. Realizar tareas de aprendizaje automático ante …

Supervised data stream mining has become an important and challenging data mining task
in modern organizations. The key challenges are threefold:(1) a possibly infinite number of …

We explore the role of sequences of extreme values for measuring tail-dependence
between times series. The proposed measure concentrates on searching extreme cause …