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Ensemble learning is being considered as one of the most well-established and efficient techniques in the contemporary machine learning. The key to the satisfactory performance of such combined models lies in the supplied base learners and selected combination strategy. In this paper we will focus on the former issue. Having classifiers that are of high individual quality and complementary to each...
Class label noise is a data-level difficulty associated with training objects with incorrectly assigned labels. This problem may originate from poorly documented historic data, errors during data generation process or mistakes made by human experts. Inclusion of such examples during the training process will mislead the classifier by presenting a falsified class distribution and consequently lead...
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...
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...
This paper proposes a method for designing Fuzzy Rule-Based Classification Systems to deal with One-Class Classification, where during the training phase we have access only to objects originating from a single class. However, the trained model must be prepared to deal with new, unseen adversarial objects, known as outliers. We use a Genetic Algorithm for learning the granularity, domains and fuzzy...
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...
One-class classification became one of the most challenging research areas of the contemporary machine learning. Contrary to canonical task here we have only information about a single class at our disposal. Therefore more sophisticated methodologies, that are able to handle all the nuisances of the target distribution are required. Fuzzy logic seems an attractive solution to handle imprecision and...
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...
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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