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Analysis of Feature Selection and Ensemble Classifier Methods for Intrusion Detection

Vinutha, H.P.; Basavaraju, Poornima - In: International Journal of Natural Computing Research (IJNCR) 7 (2018) 1, pp. 57-72

features are removed to reduce the dimension of the data. Ensemble classification techniques like Boosting, Bagging, Stacking …

Persistent link: https://www.econbiz.de/10012046727

Forecasting low-visibility procedure states with tree-based statistical methods

Dietz, Sebastian J.; Kneringer, Philipp; Mayr, Georg J.; … - 2017

predictions when new observations arrive. The tree-based ensemble method "boosting" provides the highest benefit in forecast …

Persistent link: https://www.econbiz.de/10011869142

Ensemble learning or deep learning? Application to default risk analysis

Hamori, Shigeyuki; Kawai, Minami; Kume, Takahiro; … - In: Journal of Risk and Financial Management 11 (2018) 1, pp. 1-14

three ensemble-learning methods-specifically, bagging, random forest, and boosting-with those of various neural … boosting is superior to other machine-learning methods including neural networks. It is also found that the performance of …

Persistent link: https://www.econbiz.de/10012611000

An ensemble classifier-based scoring model for predicting bankruptcy of Polish companies in the Podkarpackie Voivodeship

Pisula, Tomasz - In: Journal of Risk and Financial Management 13 (2020) 2, pp. 1-35

of a number of ensemble classifier model variants (boosting, bagging and stacking) was estimated and verified. The …

Persistent link: https://www.econbiz.de/10012611247

Machine learning advances for time series forecasting

Masini, Ricardo P.; Medeiros, Marcelo C.; Mendes, Eduardo F. - 2020

In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The...

Persistent link: https://www.econbiz.de/10012817069