Wyniki wyszukiwania - Biblioteka Nauki

1

Predictive Business Process Monitoring with Tree-based Classification Algorithms

100%

Owczarek T. , Janke P.

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2018

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tom 40

73-82

EN

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.

2

Enhancing the performance of deep learning technique by combining with gradient boosting in rainfall-runoff simulation

100%

Abdullaeva B. S.

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tom no. 59

216--223

EN

Artificial neural networks are widely employed as data mining methods by researchers across various fields, including rainfall-runoff (R-R) statistical modelling. To enhance the performance of these networks, deep learning (DL) neural networks have been developed to improve modelling accuracy. The present study aims to improve the effectiveness of DL networks in enhancing the performance of artificial neural networks via merging with the gradient boosting (GB) technique for daily runoff data forecasting in the river Amu Darya, Uzbekistan. The obtained results showed that the new hybrid proposed model performed exceptionally well, achieving a 16.67% improvement in determination coefficient (R2) and a 23.18% reduction in root mean square error (RMSE) during the training phase compared to the single DL model. Moreover, during the verification phase, the hybrid model displayed remarkable performance, demonstrating a 66.67% increase in R2 and a 50% reduction in RMSE. Furthermore, the hybrid model outperformed the single GB model by a significant margin. During the training phase, the new model showed an 18.18% increase in R2 and a 25% reduction in RMSE. In the verification phase, it improved by an impressive 75% in R2 and a 33.33% reduction in RMSE compared to the single GB model. These findings highlight the potential of the hybrid DL-GB model in improving daily runoff data forecasting in the challenging hydrological context of the Amu Darya River basin in Uzbekistan.

3

Multiple Additive Regression Trees (MART) and their Application

100%

Trzęsiok J. , Department of Mathematics K. A. U. o. E.

Acta Universitatis Lodziensis. Folia Oeconomica

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2007

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tom 206

EN

Multiple additive regression trees MART is a methodology for trying to solve prediction problems in regression and classification. It’s one of the boosting methods. It was introduced by J. H. Friedman (1999a). Besides accuracy, its primary goal is robustness. It lends to be resistant against outliers, missing values, and the inclusion of potentially large numbers of irrelevant predictor variables that have little or no effect on the response. In this paper the MART algorithm and their applications will be discussed.

PL

Addytywna metoda budowy drzew regresyjnych (MART), została zaproponowana przez J. H. Friedmana w 1999 r. (1999a, b). Jest to jedna z metod agregacyjnych, mająca zastosowanie w regresji i dyskryminacji opierająca się na modelach w postaci drzew. Jej zaletami, poza dokładnością predykcji, jest odporność na wartości oddalone i braki danych. Bardzo dobrze radzi sobie również z dużą liczbą zmiennych objaśniających, wśród których wiele może nie mieć istotnego wpływu na zmienną zależną. W artykule przedstawiona została ogólna idea metod agregacyjnych. Zaprezentowano i omówiono kolejne kroki algorytmu MART, a następnie, dla ilustracji, podany został przykład zastosowania procedury MART dla zbioru danych „Boston”.

4

FAMILIES OF CLASSIFIERS – APPLICATION IN DATA

86%

Grzybowska U. , Karwański M.

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2014

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tom 15

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nr 2

94-101

EN

Economic description of firms and companies is based on a number of indicators. The indicators are related to each other and can be considered only in a specific context. Regression models allow for such approach. Unfortunately, the problems we deal with are usually nonlinear and the choice of relevant information is very difficult. The aim of the paper is to present a method of variable selection based on random forest and gradient boosting approach and its application to companies ranking in DEA method. The results will be compared with the ordering obtained using expert supported approach for variable selection in DEA.

5

A comparative study of empirical and ensemble machine learning algorithms in predicting air over pressure in open pit coal mine

72%

Nguyen H. , Bui X.-N. , Tran Q.-H. , Pham V. H. , Nguyen D.-A. , Le T. T. H. , Le Q.-T. , Do N.-H. , Bao T. D. , Bui H.-B. , Moayedi H.

Acta Geophysica

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2020

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tom Vol. 68, no. 2

325--336

EN

This study aims to take into account the feasibility of three ensemble machine learning algorithms for predicting blast-induced air over-pressure (AOp) in open-pit mine, including gradient boosting machine (GBM), random forest (RF), and Cubist. An empirical technique was also applied to predict AOp and compared with those of the ensemble models. To employ this study, 146 events of blast were investigated with 80% of the total database (approximately 118 blasting events) being used for developing the models, whereas the rest (20%~28 blasts) were used to validate the models’ accuracy. RMSE, MAE, and R2 were used as performance indices for evaluating the reliability of the models. The fndings revealed that the ensemble models yielded more precise accuracy than those of the empirical model. Of the ensemble models, the Cubist model provided better performance than those of RF and GBM models with RMSE, MAE, and R2 of 2.483, 0.976, and 0.956, respectively, whereas the RF and GBM models provided poorer accuracy with an RMSE of 2.579, 2.721; R2 of 0.953, 0.950, and MAE of 1.103, 1.498, respectively. In contrast, the empirical model was interpreted as the poorest model with an RMSE of 4.448, R2 of 0.872, and MAE of 3.719. In addition, other fndings indicated that explosive charge capacity, spacing, stemming, monitoring distance, and air humidity were the most important inputs for the AOp predictive models using artifcial intelligence.