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    Tytuł:
    Some Nonparametric Estimators of Regression Function
    Wybrane nieparametryczne estymatory funkcji regresji
    Autorzy:
    Baszczyńska, Aleksandra
    Powiązania:
    https://bibliotekanauki.pl/articles/906895.pdf
    Data publikacji:
    2007
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    regression function
    kernel function
    smoothing parameter
    k-nearest neighbour method
    kernel method
    Opis:
    In the paper some nonparametric estimators of regression function are studied: Nadaraya-Watson estimator and k-nearest neighbour one. Properties of these estimators and possibilities of using them in practice are taken into consideration. A comparative study of the two estimators is presented. Different techniques of choosing method’s parameters (kernel function, smoothing parameter h and parameter k) are used in this study to choose the optimal ones. Some practical rules are proposed and they are used in this study.
    W pracy przedstawiono wybrane dwa nieparametryczne estymatory funkcji regresji: estymator jądrowy Nadaraya-Watsona oraz estymator k-najbliższego sąsiada. Podano ich własności, możliwości wykorzystania oraz dokonano porównania tych estymatorów. Przedstawiono również przykład zastosowania estymatora jądrowego regresji z uwzględnieniem właściwego doboru parametrów metody (funkcji jądra i parametru wygładzania h) oraz estymatora k-najbliższego sąsiada z uwzględnieniem właściwego doboru parametru k. Zaproponowano również praktyczne zasady wyboru parametrów estymacji funkcji regresji i wykorzystano je w przykładzie.
    Źródło:
    Acta Universitatis Lodziensis. Folia Oeconomica; 2007, 206
    0208-6018
    2353-7663
    Pojawia się w:
    Acta Universitatis Lodziensis. Folia Oeconomica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An adaptive k nearest neighbour method for imputation of missing traffic data based on two similarity metrics
    Autorzy:
    Wang, Yang
    Xiao, Yu
    Lai, Jianhui
    Chen, Yanyan
    Powiązania:
    https://bibliotekanauki.pl/articles/949848.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    missing traffic data
    similarity metrics
    K-nearest neighbour method
    stochastic characteristics
    metoda porównywania danych
    metryki podobieństwa
    metoda najbliższego sąsiada
    cechy stochastyczne
    Opis:
    Traffic flow is one of the fundamental parameters for traffic analysis and planning. With the rapid development of intelligent transportation systems, a large number of various detectors have been deployed in urban roads and, consequently, huge amount of data relating to the traffic flow are accumulatively available now. However, the traffic flow data detected through various detectors are often degraded due to the presence of a number of missing data, which can even lead to erroneous analysis and decision if no appropriate process is carried out. To remedy this issue, great research efforts have been made and subsequently various imputation techniques have been successively proposed in recent years, among which the k nearest neighbour algorithm (kNN) has received a great popularity as it is easy to implement and impute the missing data effectively. In the work presented in this paper, we firstly analyse the stochastic effect of traffic flow, to which the suffering of the kNN algorithm can be attributed. This motivates us to make an improvement, while eliminating the requirement to predefine parameters. Such a parameter-free algorithm has been realized by introducing a new similarity metric which is combined with the conventional metric so as to avoid the parameter setting, which is often determined with the requirement of adequate domain knowledge. Unlike the conventional version of the kNN algorithm, the proposed algorithm employs the multivariate linear regression model to estimate the weights for the final output, based on a set of data, which is smoothed by a Wavelet technique. A series of experiments have been performed, based on a set of traffic flow data reported from serval different countries, to examine the adaptive determination of parameters and the smoothing effect. Additional experiments have been conducted to evaluate the competent performance for the proposed algorithm by comparing to a number of widely-used imputing algorithms.
    Źródło:
    Archives of Transport; 2020, 54, 2; 59-73
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways
    Autorzy:
    Lee, Y.
    Wei, C.-H.
    Chao, K.-C.
    Powiązania:
    https://bibliotekanauki.pl/articles/223569.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    accident on freeway
    accident duration
    effect evaluating
    correlation
    artificial neural networks
    k-nearest neighbour method
    wypadek na autostradzie
    czas trwania wypadku
    ocena skutków
    korelacja
    sztuczne sieci neuronowe
    metoda najbliższego sąsiada
    Opis:
    Traffic accidents usually cause congestion and increase travel-times. The cost of extra travel time and fuel consumption due to congestion is huge. Traffic operators and drivers expect an accurately forecasted accident duration to reduce uncertainty and to enable the implementation of appropriate strategies. This study demonstrates two non-parametric machine learning methods, namely the k-nearest neighbour method and artificial neural network method, to construct accident duration prediction models. The factors influencing the occurrence of accidents are numerous and complex. To capture this phenomenon and improve the performance of accident duration prediction, the models incorporated various data including accident characteristics, traffic data, illumination, weather conditions, and road geometry characteristics. All raw data are collected from two public agencies and were integrated and cross-checked. Before model development, a correlation analysis was performed to reduce the scale of interrelated features or variables. Based on the performance comparison results, an artificial neural network model can provide good and reasonable prediction for accident duration with mean absolute percentage error values less than 30%, which are better than the prediction results of a k-nearest neighbour model. Based on comparison results for circumstances, the Model which incorporated significant variables and employed the ANN method can provide a more accurate prediction of accident duration when the circumstances involved the day time or drunk driving than those that involved night time and did not involve drunk driving. Empirical evaluation results reveal that significant variables possess a major influence on accident duration prediction.
    Źródło:
    Archives of Transport; 2017, 43, 3; 91-104
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A differential evolution approach to dimensionality reduction for classification needs
    Autorzy:
    Martinović, G.
    Bajer, D.
    Zorić, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/331498.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    classification
    differential evolution
    feature subset selection
    k-nearest neighbour algorithm
    wrapper method
    ewolucja różnicowa
    selekcja cech
    algorytm najbliższego sąsiada
    Opis:
    The feature selection problem often occurs in pattern recognition and, more specifically, classification. Although these patterns could contain a large number of features, some of them could prove to be irrelevant, redundant or even detrimental to classification accuracy. Thus, it is important to remove these kinds of features, which in turn leads to problem dimensionality reduction and could eventually improve the classification accuracy. In this paper an approach to dimensionality reduction based on differential evolution which represents a wrapper and explores the solution space is presented. The solutions, subsets of the whole feature set, are evaluated using the k-nearest neighbour algorithm. High quality solutions found during execution of the differential evolution fill the archive. A final solution is obtained by conducting k-fold cross-validation on the archive solutions and selecting the best one. Experimental analysis is conducted on several standard test sets. The classification accuracy of the k-nearest neighbour algorithm using the full feature set and the accuracy of the same algorithm using only the subset provided by the proposed approach and some other optimization algorithms which were used as wrappers are compared. The analysis shows that the proposed approach successfully determines good feature subsets which may increase the classification accuracy.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 111-122
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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