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    Wyświetlanie 1-3 z 3
    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
    Tytuł:
    Data mining methods for prediction of air pollution
    Autorzy:
    Siwek, K.
    Osowski, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/330775.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    computational intelligence
    feature selection
    neural network
    random forest
    air pollution forecasting
    inteligencja obliczeniowa
    selekcja cech
    sieć neuronowa
    lasy losowe
    zanieczyszczenie powietrza
    Opis:
    The paper discusses methods of data mining for prediction of air pollution. Two tasks in such a problem are important: generation and selection of the prognostic features, and the final prognostic system of the pollution for the next day. An advanced set of features, created on the basis of the atmospheric parameters, is proposed. This set is subject to analysis and selection of the most important features from the prediction point of view. Two methods of feature selection are compared. One applies a genetic algorithm (a global approach), and the other—a linear method of stepwise fit (a locally optimized approach). On the basis of such analysis, two sets of the most predictive features are selected. These sets take part in prediction of the atmospheric pollutants PM10, SO2, NO2 and O3. Two approaches to prediction are compared. In the first one, the features selected are directly applied to the random forest (RF), which forms an ensemble of decision trees. In the second case, intermediate predictors built on the basis of neural networks (the multilayer perceptron, the radial basis function and the support vector machine) are used. They create an ensemble integrated into the final prognosis. The paper shows that preselection of the most important features, cooperating with an ensemble of predictors, allows increasing the forecasting accuracy of atmospheric pollution in a significant way.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2016, 26, 2; 467-478
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble neural network approach for accurate load forecasting in a power system
    Autorzy:
    Siwek, K.
    Osowski, S.
    Szupiluk, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/907659.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sieć neuronowa
    ślepa separacja sygnałów
    prognozowanie obciążenia
    neural network
    blind source separation
    ensemble of predictors
    load forecasting
    Opis:
    The paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2009, 19, 2; 303-315
    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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