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    Wyświetlanie 1-4 z 4
    Tytuł:
    Application of selected data mining techniques in unintentional accounting error detection
    Autorzy:
    Papík, Mário
    Papíková, Lenka
    Powiązania:
    https://bibliotekanauki.pl/articles/22444352.pdf
    Data publikacji:
    2021
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    financial fraud
    unintentional accounting errors
    financial restatements
    decision tree
    classification and regression tree
    random forest
    Opis:
    Research background: Even though unintentional accounting errors leading to financial restatements look like less serious distortion of publicly available information, it has been shown that financial restatements impacts on financial markets are similar to intentional fraudulent activities. Unintentional accounting errors leading to financial restatements then affect value of company shares in the short run which negatively impacts all shareholders. Purpose of the article: The aim of this manuscript is to predict unintentional accounting errors leading to financial restatements based on information from financial statements of companies. The manuscript analysis if financial statements include sufficient information which would allow detection of unintentional accounting errors. Methods: Method of classification and regression trees (decision tree) and random forest have been used in this manuscript to fulfill the aim of this manuscript. Data sample has consisted of 400 items from financial statements of 80 selected international companies. The results of developed prediction models have been compared and explained based on their accuracy, sensitivity, specificity, precision and F1 score. Statistical relationship among variables has been tested by correlation analysis. Differences between the group of companies with and without unintentional accounting error have been tested by means of Kruskal-Wallis test. Differences among the models have been tested by Levene and T-tests. Findings & value added: The results of the analysis have provided evidence that it is possible to detect unintentional accounting errors with high levels of accuracy based on financial ratios (rather than the Beneish variables) and by application of random forest method (rather than classification and regression tree method).
    Źródło:
    Equilibrium. Quarterly Journal of Economics and Economic Policy; 2021, 16, 1; 185-201
    1689-765X
    2353-3293
    Pojawia się w:
    Equilibrium. Quarterly Journal of Economics and Economic Policy
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Maximising accuracy and efficiency of traffic accident prediction combining information mining with computational intelligence approaches and decision trees
    Autorzy:
    Tambouratzis, T>
    Souliou, D.
    Chalikias, M.
    Gregoriades, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/91652.pdf
    Data publikacji:
    2014
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    traffic accident
    location
    prediction
    probabilistic neural networks
    random forest
    accuracy
    efficiency
    decision tree
    Opis:
    The development of universal methodologies for the accurate, efficient, and timely prediction of traffic accident location and severity constitutes a crucial endeavour. In this piece of research, the best combinations of salient accident-related parameters and accurate accident severity prediction models are determined for the 2005 accident dataset brought together by the Republic of Cyprus Police. The optimal methodology involves: (a) information mining in the form of feature selection of the accident parameters that maximise prediction accuracy (implemented via scatter search), followed by feature extraction (implemented via principal component analysis) and selection of the minimal number of components that contain the salient information of the original parameters, which combined bring about an overall 74.42% reduction in the dataset dimensionality; (b) accident severity prediction via probabilistic neural networks and random forests, both of which independently accomplish over 96% correct prediction and a balanced proportion of under- and over-estimations of accident severity. An explanation of the superiority of the optimal combinations of parameters and models is given, as is a comparison with existing accident classification/prediction approaches.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 1; 31-42
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Imitation learning of car driving skills with decision trees and random forests
    Autorzy:
    Cichosz, P.
    Pawełczak, Ł.
    Powiązania:
    https://bibliotekanauki.pl/articles/329901.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    imitation learning
    behavioral cloning
    model ensemble
    random forest
    control
    autonomous driving
    car racing
    decision tree
    drzewo decyzyjne
    lasy losowe
    sterowanie
    wyścigi samochodowe
    Opis:
    Machine learning is an appealing and useful approach to creating vehicle control algorithms, both for simulated and real vehicles. One common learning scenario that is often possible to apply is learning by imitation, in which the behavior of an exemplary driver provides training instances for a supervised learning algorithm. This article follows this approach in the domain of simulated car racing, using the TORCS simulator. In contrast to most prior work on imitation learning, a symbolic decision tree knowledge representation is adopted, which combines potentially high accuracy with human readability, an advantage that can be important in many applications. Decision trees are demonstrated to be capable of representing high quality control models, reaching the performance level of sophisticated pre-designed algorithms. This is achieved by enhancing the basic imitation learning scenario to include active retraining, automatically triggered on control failures. It is also demonstrated how better stability and generalization can be achieved by sacrificing human-readability and using decision tree model ensembles. The methodology for learning control models contributed by this article can be hopefully applied to solve real-world control tasks, as well as to develop video game bots.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 579-597
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nadmierne dopasowanie w drzewach decyzyjnych
    Excessive fit in decision trees
    Autorzy:
    Smaga, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/91343.pdf
    Data publikacji:
    2011
    Wydawca:
    Warszawska Wyższa Szkoła Informatyki
    Tematy:
    drzewo decyzyjne
    kryterium doboru testu
    zbiór treningowy
    kryterium losowe
    kryterium entropijne
    decision tree
    criteria for the test selection
    trining collection
    random criterion
    entropy criterion
    Opis:
    W pracy staramy się sprawdzić wpływ jaki ma dobór kryterium wyboru testu na nadmierne dopasowanie w drzewach decyzyjnych. Uważamy, że losowe kryterium doboru może okazać się nie gorsze od kryterium entropijnego. Nasze przypuszczenia potwierdzają wstępne badania wykonane dla trzech (niewielkich rozmiarów) zbiorach trenujących, co w naszej opinii zasługuje na dalsze eksperymenty.
    In this paper we try to check the influence of selection criteria for the test selection for excessive fit in decision trees. We believe that a random criteria selection may not be worse than the criteria of entropy. Our supposition is confirmed by preliminary tests performed for three training sets, which in our opinion deserves further experiments.
    Źródło:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2011, 5, 5; 75-78
    1896-396X
    2082-8349
    Pojawia się w:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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