Bagging and boosting techniques in prediction of particulate matters

• Autorzy: Triana D. , Osowski S.

Identyfikatory

DOI

10.24425/bpasts.2020.134659

• Języki publikacji: EN

Abstrakty

EN

The paper presents new ensemble solutions, which can forecast the average level of particulate matters PM10 and PM2.5 with increased accuracy. The proposed network is composed of weak predictors integrated into a final expert system. The members of the ensemble are built based on deep multilayer perceptron and decision tree and use bagging and boosting principle in elaborating common decisions. The numerical experiments have been carried out for prediction of daily average pollution of PM10 and PM2.5 for the next day. The results of experiments have shown, that bagging and boosting ensembles employing these weak predictors improve greatly the quality of results. The mean absolute errors have been reduced by more than 30% in the case of PM10 and 20% in the case of PM2.5 in comparison to individually acting predictors.

Słowa kluczowe

EN

ensemble of predictors; bagging; boosting; PM pollution

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2020
• Tom: Vol. 68, nr 5
• Strony: 1207--1215
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Triana D.

• Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland

autor

Osowski S.

• Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland
• Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland

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