Next Article in Journal
Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining
Previous Article in Journal
Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs
Previous Article in Special Issue
Innovative Use of Wrist-Worn Wearable Devices in the Sports Domain: A Systematic Review
Open AccessArticle

Weighted Random Forests to Improve Arrhythmia Classification

1
Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences - SGGW, 02-776 Warsaw, Poland
2
Department of Physics of Complex Systems, Faculty of Physics, Warsaw University of Technology, 00-662 Warsaw, Poland
3
Department of Computer Science, Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX 78712, USA
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(1), 99; https://doi.org/10.3390/electronics9010099
Received: 2 December 2019 / Revised: 30 December 2019 / Accepted: 31 December 2019 / Published: 3 January 2020
Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each tree in the Random Forest model and the comprehensive examination of the optimal parameter tuning. Importantly, the approach is motivated by its flexibility, good performance, stability, and resistance to overfitting. The proposed scheme is examined and evaluated on the Physionet/Computing in Cardiology Challenge 2015 data set. It consists of signals (electrocardiograms and pulsatory waveforms) from intensive care patients which triggered an alarm for five cardiac arrhythmia types (Asystole, Bradycardia, Tachycardia, Ventricular Tachycardia, and Ventricular Fultter/Fibrillation). The classification problem regards whether the alarm should or should not have been generated. It was proved that the proposed weighting approach improved classification accuracy for the three most challenging out of the five investigated arrhythmias comparing to the standard Random Forest model. View Full-Text
Keywords: arrhythmia; false alarm; weighted random forest; machine learning arrhythmia; false alarm; weighted random forest; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Gajowniczek, K.; Grzegorczyk, I.; Ząbkowski, T.; Bajaj, C. Weighted Random Forests to Improve Arrhythmia Classification. Electronics 2020, 9, 99.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop