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Modern computer systems generate massive amounts of data in real-time. We have come to the age of big data, where the amount of information exceeds the perceptive abilities of any human being. Frequently the massive data collections arrive over time, in the form of a data stream. Not only the volume and velocity of data poses a challenge for machine learning systems, but also its variability. Such...
Texture analysis and classification play an important role in many multimedia and computer vision applications. Local binary patterns (LBP) form a simple yet powerful texture descriptor characterising local neighbourhood properties, and consequently LBP variants are widely employed. In this paper, we demonstrate that through appropriate construction of a multiple classifier system, improved texture...
Classification of imbalanced data represents a challenging task in machine learning, as most classification algorithms tend to bias towards the majority class, while often correctly identifying minority class instances is of greater importance. Consequently, there is a need for methods that provide improved accuracy for the minority class without sacrificing overall performance. Ensemble classification...
One-class classification is one of the most challenging topics in the field of machine learning. Recently creating Multiple Classifier Systems for this task has proven itself as a promising research direction. Here arises a problem on how to select valuable members to the committee — so far a largely unexplored area in one-class classification. This paper introduced a novel approach that allows to...
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