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    Wyświetlanie 1-5 z 5
    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 rainfall forecasting method using machine learning models and its application to the Fukuoka city case
    Autorzy:
    Sumi, S. M.
    Zaman, M. F.
    Hirose, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/331290.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    maszyna ucząca się
    metoda wielomodelowa
    przetwarzanie wstępne
    rainfall forecasting
    machine learning
    multi model method
    preprocessing
    model ranking
    Opis:
    In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the k-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 841-854
    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
    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:
    Niching mechanisms in evolutionary computations
    Autorzy:
    Kowalczuk, Z.
    Białaszewski, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/908461.pdf
    Data publikacji:
    2006
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    algorytm genetyczny
    projektowanie inżynierskie
    obliczenia ewolucyjne
    obserwator detekcyjny
    niching
    ranking
    Pareto optimality
    genetic algorithms
    evolutionary computations
    multi-objective optimisation
    solutions diversity
    engineering designs
    detection observers
    Opis:
    Different types of niching can be used in genetic algorithms (GAs) or evolutionary computations (ECs) to sustain the diversity of the sought optimal solutions and to increase the effectiveness of evolutionary multi-objective optimization solvers. In this paper four schemes of niching are proposed, which are also considered in two versions with respect to the method of invoking: a continuous realization and a periodic one. The characteristics of these mechanisms are discussed, while as their performance and effectiveness are analyzed by considering exemplary multi-objective optimization tasks both of a synthetic and an engineering (FDI) design nature.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2006, 16, 1; 59-84
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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