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    Wyświetlanie 1-2 z 2
    Tytuł:
    Automated approach to classification of mine-like objects using multiple-aspect sonar images
    Autorzy:
    Wang, X.
    Liu, X.
    Japkowicz, N.
    Matwin, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/91790.pdf
    Data publikacji:
    2014
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    object
    sea bed
    multiple side-scan sonar
    Dempster-Shafer theory
    DS concept
    classifier
    imbalance
    imbalanced problem
    multi-instance class
    Opis:
    In this paper, the detection of mines or other objects on the seabed from multiple side-scan sonar views is considered. Two frameworks are provided for this kind of classification. The first framework is based upon the Dempster–Shafer (DS) concept of fusion from a single-view kernel-based classifier and the second framework is based upon the concepts of multi-instance classifiers. Moreover, we consider the class imbalance problem which is always presents in sonar image recognition. Our experimental results show that both of the presented frameworks can be used in mine-like object classification and the presented methods for multi-instance class imbalanced problem are also effective in such classification.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 133-148
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The use of data mining models in solving the problem of imbalanced classes based on the example of an online marketing campaign
    Wykorzystanie modeli data mining w rozwiązywaniu problemu niezrównoważonych klas na przykładzie kampanii marketingowych w Internecie
    Autorzy:
    Łapczyński, Mariusz
    Surma, Jerzy
    Powiązania:
    https://bibliotekanauki.pl/articles/424980.pdf
    Data publikacji:
    2015
    Wydawca:
    Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
    Tematy:
    C&RT
    Random Forest
    imbalanced class problem
    online social network
    banner ad campaign
    Opis:
    While building predictive models in analytical CRM, researchers often encounter the problem of imbalanced classes (skewed distributions of dependent variables), which consists in the fact that the number of observations belonging to one category of the dependent variable is much lower than the number of observations belonging to the second category of that variable. This is related to such areas as churn analysis, customer acquisition models and cross and up-selling models. The purpose of the paper is to present a predictive model that was built to predict the response of Internet users to banner advertising. The dataset used in the study came from an online social network which offers advertisers banner campaigns targeting its users. The advertising campaign of a cosmetics company was carried out in the autumn of 2010 and was mainly targeted at young women. A user of this service was described by 115 independent variables – 3 out of which were demographic variables (sex, age, education), and the remaining 112 referred to the user’s online activity. While building the model there appeared the problem of imbalanced classes due to the low number of users who clicked on the banner ad. The number of cases amounted to 81,000, while the number of positive reactions to the banner was 207, which constitutes approximately 0.25% of the dependent variable. During the study, two popular data mining tools were utilized – the decision trees C&RT and Random Forest. The second goal of this paper is to compare the performance of the predictive models based on both these analytical tools.
    Źródło:
    Econometrics. Ekonometria. Advances in Applied Data Analytics; 2015, 3 (49); 9-19
    1507-3866
    Pojawia się w:
    Econometrics. Ekonometria. Advances in Applied Data Analytics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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