Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

Incremental learning refers to learning from streaming data, which arrive over time, with
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …

Scientific simulation can generate petabyte-level data per run nowadays. To significantly
reduce the data size while simultaneously maintaining the compression quality based on …

Graph-based algorithms have been successfully applied in machine learning and data
mining tasks. A simple but, widely used, approach to build graphs from vector-based data is …

The performance of classification algorithms is highly dependent on the quality of training
data. Missing attribute values are quite common in many real world applications, thus, in …

Attribute-based Decision Graphs (AbDG) have been recently proposed as a novel and
effective way to represent data as weighted labeled graphs. However, for some domains, the …

Scientific simulation can generate petabyte-level data per run nowadays. To significantly
reduce the data size while simultaneously maintaining the compression quality based on …

Efficient automatic systems which continuously learn over long periods of time, and manage
to evolve its knowledge, by discarding obsolete parts of it and acquiring new ones to reflect …

Abstract Current sensor-based Human Activity Recognition (HAR) techniques that rely on a
user-independent model struggle to generalize to new users and on to changes that a …

Missing attribute values is a recurrent problem in data mining and machine learning.
Although there are plenty of techniques to handle this problem, most of them are too …