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    Wyświetlanie 1-3 z 3
    Tytuł:
    On the order equivalence relation of binary association measures
    Autorzy:
    Paradowski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/330881.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    association coefficient
    result ranking
    linear combination
    zeroed variance determinant
    feature selection
    Opis:
    Over a century of research has resulted in a set of more than a hundred binary association measures. Many of them share similar properties. An overview of binary association measures is presented, focused on their order equivalences. Association measures are grouped according to their relations. Transformations between these measures are shown, both formally and visually. A generalization coefficient is proposed, based on joint probability and marginal probabilities. Combining association measures is one of recent trends in computer science. Measures are combined in linear and nonlinear discrimination models, automated feature selection or construction. Knowledge about their relations is particularly important to avoid problems of meaningless results, zeroed generalized variances, the curse of dimensionality, or simply to save time.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2015, 25, 3; 645-657
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A weighted wrapper approach to feature selection
    Autorzy:
    Kusy, Maciej
    Zajdel, Roman
    Powiązania:
    https://bibliotekanauki.pl/articles/2055180.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    feature selection
    wrapper approach
    feature significance
    weighted combined ranking
    convolutional neural network
    classification accuracy
    selekcja cech
    sieć neuronowa konwolucyjna
    dokładność klasyfikacji
    Opis:
    This paper considers feature selection as a problem of an aggregation of three state-of-the-art filtration methods: Pearson’s linear correlation coefficient, the ReliefF algorithm and decision trees. A new wrapper method is proposed which, on the basis of a fusion of the above approaches and the performance of a classifier, is capable of creating a distinct, ordered subset of attributes that is optimal based on the criterion of the highest classification accuracy obtainable by a convolutional neural network. The introduced feature selection uses a weighted ranking criterion. In order to evaluate the effectiveness of the solution, the idea is compared with sequential feature selection methods that are widely known and used wrapper approaches. Additionally, to emphasize the need for dimensionality reduction, the results obtained on all attributes are shown. The verification of the outcomes is presented in the classification tasks of repository data sets that are characterized by a high dimensionality. The presented conclusions confirm that it is worth seeking new solutions that are able to provide a better classification result while reducing the number of input features.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 685--696
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Data mining methods for gene selection on the basis of gene expression arrays
    Autorzy:
    Muszyński, M.
    Osowski, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/329803.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    gene expression array
    gene ranking
    feature selection
    clusterization measures
    fusion
    SVM classification
    ekspresja genów
    selekcja cech
    klasyfikacja SVM
    Opis:
    The paper presents data mining methods applied to gene selection for recognition of a particular type of prostate cancer on the basis of gene expression arrays. Several chosen methods of gene selection, including the Fisher method, correlation of gene with a class, application of the support vector machine and statistical hypotheses, are compared on the basis of clustering measures. The results of applying these individual selection methods are combined together to identify the most often selected genes forming the required pattern, best associated with the cancerous cases. This resulting pattern of selected gene lists is treated as the input data to the classifier, performing the task of the final recognition of the patterns. The numerical results of the recognition of prostate cancer from normal (reference) cases using the selected genes and the support vector machine confirm the good performance of the proposed gene selection approach.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 657-668
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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