Multilabel classification is a challenging research problem in which each instance is assigned to a subset of labels. Recently, a considerable amount of research has been concerned...
Muhammad Atif Tahir, Josef Kittler, Krystian Mikol...
As data streams are gaining prominence in a growing number of emerging application domains, classification on data streams is becoming an active research area. Currently, the typi...
— Feature selection and ensemble classification increase system efficiency and accuracy in machine learning, data mining and biomedical informatics. This research presents an ana...
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal ...