Instance selection is becoming increasingly relevant because of the large amount of data
that is constantly being produced in many fields of research. Two basic approaches exist for …

This paper presents ContextMiner, a novel natural language processing (NLP) framework to
automatically capture contextual features for the purpose of extracting meaningful context …

The work deals with the distributed machine learning. Distributed learning from data is
considered to be an important challenge faced by researchers and practice in the domain of …

The paper reviews current research results integrating machine learning and agent
technologies. Although complementary solutions from both fields are discussed the focus is …

This paper focuses on the machine classification with data reduction. The aim of the data
reduction techniques is decreasing the quantity of information required to learn a high …

Instance selection, often referred to as data reduction, aims at deciding which instances from
the training set should be retained for further use during the learning process. Instance …

Class imbalance arises when the number of examples belonging to one class is much
greater than the number of examples belonging to another. The discussed approach …

Data reduction can increase generalization abilities of the learning model and shorten
learning time. It can be particularly helpful in analyzing big data sets. This paper focuses on …

Main impact of the paper is proposing a family of algorithms for the online learning and
classification. These algorithms work in rounds, where at each round a new instance is …

The paper referees to a problem of learning from class-imbalanced data. The class
imbalance problem arises when the number of instances from different classes differs …