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    Wyświetlanie 1-3 z 3
    Tytuł:
    Predictive Business Process Monitoring with Tree-based Classification Algorithms
    Autorzy:
    Owczarek, Tomasz
    Janke, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/503954.pdf
    Data publikacji:
    2018
    Wydawca:
    Międzynarodowa Wyższa Szkoła Logistyki i Transportu
    Tematy:
    business process
    prediction
    classification
    random forest
    gradient boosting
    Opis:
    Predictive business process monitoring is a current research area which purpose is to predict the outcome of a whole process (or an element of a process i.e. a single event or task) based on available data. In the article we explore the possibility of use of the machine learning classification algorithms based on trees (CART, C5.0, random forest and extreme gradient boosting) in order to anticipate the result of a process. We test the application of these algorithms on real world event-log data and compare it with the known approaches. Our results show that.
    Źródło:
    Logistics and Transport; 2018, 40, 4; 73-82
    1734-2015
    Pojawia się w:
    Logistics and Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble-based Method of Fraud Detection at Self-checkouts in Retail
    Autorzy:
    Vitynskyi, P.
    Tkachenko, R.
    Izonin, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/410756.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    classification
    Ensemble-based method
    Random Forest
    fraud detection
    retail
    Ito decomposition
    imbalanced dataset
    Opis:
    The authors consider the problem of fraud detection at self-checkouts in retail in condition of unbalanced data set. A new ensemble-based method is proposed for its effective solution. The developed method involves two main steps: application of the preprocessing procedures and the Random Forest algorithm. The step-by-step implementation of the preprocessing stage involves the sequential execution of such procedures over the input data: scaling by maximal element in a column with row-wise scaling by Euclidean norm, weighting by correlation and applying polynomial extension. For polynomial extension Ito decomposition of the second degree is used. The simulation of the method was carried out on real data. Evaluating performance was based on the use of cost matrix. The experimental comparison of the effectiveness of the developed ensemble-based method with a number of existing (simples and ensembles) demonstrates the best performance of the developed method. Experimental studies of changing the parameters of the Random Forest both for the basic algorithm and for the developed method demonstrate a significant improvement of the investigated efficiency measures of the latter. It is the result of all steps of the preprocessing stage of the developed method use.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2019, 8, 2; 3-8
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A comparative study on performance of basic and ensemble classifiers with various datasets
    Autorzy:
    Gunakala, Archana
    Shahid, Afzal Hussain
    Powiązania:
    https://bibliotekanauki.pl/articles/30148255.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    classification
    Naïve Bayes
    neural network
    Support Vector Machine
    Decision Tree
    ensemble learning
    Random Forest
    Opis:
    Classification plays a critical role in machine learning (ML) systems for processing images, text and high -dimensional data. Predicting class labels from training data is the primary goal of classification. An optimal model for a particular classification problem is chosen based on the model's performance and execution time. This paper compares and analyzes the performance of basic as well as ensemble classifiers utilizing 10-fold cross validation and also discusses their essential concepts, advantages, and disadvantages. In this study five basic classifiers namely Naïve Bayes (NB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) and the ensemble of all the five classifiers along with few more combinations are compared with five University of California Irvine (UCI) ML Repository datasets and a Diabetes Health Indicators dataset from Kaggle repository. To analyze and compare the performance of classifiers, evaluation metrics like Accuracy, Recall, Precision, Area Under Curve (AUC) and F-Score are used. Experimental results showed that SVM performs best on two out of the six datasets (Diabetes Health Indicators and waveform), RF performs best for Arrhythmia, Sonar, Tic-tac-toe datasets, and the best ensemble combination is found to be DT+SVM+RF on Ionosphere dataset having respective accuracies 72.58%, 90.38%, 81.63%, 73.59%, 94.78% and 94.01%. The proposed ensemble combinations outperformed the conven¬tional models for few datasets.
    Źródło:
    Applied Computer Science; 2023, 19, 1; 107-132
    1895-3735
    2353-6977
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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