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Contemporary machine learning systems must be able to deal with ever-growing volumes of data. However, most of the canonical classifiers are not well-suited for big data analytics. This is especially vivid in case of distance-based classifiers, where their classification time is prohibitive. Recently, many methods for adapting nearest neighbour classifier for big data were proposed. We investigate...
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...
Imbalanced classification is one of the most challenging machine learning problem. Recent studies show, that often the uneven ratio of objects in classes is not the biggest factor, determining the drop of classification accuracy. It is also related to some difficulties embedded in the nature of the data. In this paper we study the different types of minority class examples and distinguish four groups...
Nowadays many researches related to classifier design are trying to exploit strength of the ensemble learning. Such hybrid approach looks for the valuable combination of individual classifiers’ outputs, which should at least outperforms quality of the each available individuals. Therefore the classifier ensembles are recently the focus of intense research. Basically, it faces with two main problems...
This paper introduces a novel method for forming efficient one-class classifier ensembles. A common problem in one-class classification is a complex structure of the target class, which often leads to creation of a too expanded decision boundary. We propose to employ a clustering step in order to partition the target class into atomic subsets and using these as input for one-class classifiers. By...
Indirect immunofluorescence imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells. This is particularly useful for the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells can be categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and Centro mere cells, which...
Medical thermography has been demonstrated an effective and inexpensive method for detecting breast cancer, in particular for tumors in early stages and in dense tissue. Image features can be extracted from breast thermo grams and used in a pattern classification stage for automated diagnosis and hence as a second objective opinion or for screening purposes. One of the main challenges for applying...
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 is a challenging task in machine learning, as most classification approaches tend to bias towards the majority class, even though the minority class is often the one of greater importance. Consequently, methods that are capable of boosting the classification accuracy on the minority class are sought after. In this paper, we propose an improved ensemble approach for...
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...
The advance of high-throughput techniques, such as gene microarrays and protein chips have a major impact on contemporary biology and medicine. Due to the high-dimensionality and complexity of the data, it is impossible to analyze it manually. Therefore machine learning techniques play an important role in dealing with such data. In this paper we propose to use a one-class approach to classifying...
Breast cancer is the most commonly diagnosed form of cancer in women. Thermography, which uses cameras with sensitivities in the thermal infrared, has been shown to provide an interesting modality for detecting breast cancer as it is able to detect small tumors and hence can lead to earlier diagnosis. In this paper, we present an effective approach to breast thermogram analysis that utilises features...
The paper investigates the influence of different types of distance measures on the performance of a multiple classifier system consisting of one-class classifiers. This specific type of machine learning approach uses examples only from a single class to derive a decision boundary — hence its is often referred to as learning in the absence of counterexamples. Combining several one-class classifiers...
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