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Wyszukujesz frazę "fuzzy classification" wg kryterium: Temat

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    Wyświetlanie 1-3 z 3
    Tytuł:
    Design of fuzzy rule-based classifiers through granulation and consolidation
    Autorzy:
    Riid, A.
    Preden, J.-S.
    Powiązania:
    https://bibliotekanauki.pl/articles/91638.pdf
    Data publikacji:
    2017
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    pattern recognition
    fuzzy classification
    complexity reduction
    Opis:
    This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 2; 137-147
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Self-configuring hybrid evolutionary algorithm for fuzzy imbalanced classification with adaptive instance selection
    Autorzy:
    Stanovov, V.
    Semenkin, E.
    Semenkina, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/91578.pdf
    Data publikacji:
    2016
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    fuzzy classification
    instance selection
    genetic fuzzy system
    self-configuration
    Opis:
    A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 3; 173-188
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel approach for automatic detection and classification of suspicious lesions in breast ultrasound images
    Autorzy:
    Karimi, B.
    Krzyżak, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/91890.pdf
    Data publikacji:
    2013
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    automatic detection
    classification
    breast cancer
    cancer lesions
    ultrasound images
    AdaBoost
    artificial neural network
    Fuzzy Support Vector Machine
    Opis:
    In this research, a new method for automatic detection and classification of suspected breast cancer lesions using ultrasound images is proposed. In this fully automated method, de-noising using fuzzy logic and correlation among ultrasound images taken from different angles is used. Feature selection using combination of sequential backward search, sequential forward search and distance-based methods is obtained. A new segmentation method based on automatic selection of seed points and region growing is proposed and classification of lesions into two malignant and benign classes using combination of AdaBoost, Artificial Neural Network and Fuzzy Support Vector Machine classifiers and majority voting is implemented.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 4; 265-276
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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