Optimized random forest classifier for drone pilot identification

Aysha Khaled Alharam, Abdulhadi Shoufan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Random forest is a powerful machine learning scheme which finds applications in real-time systems such as unmanned aerial vehicles. In such applications not only the classification performance is relevant but also several non-functional requirements including the classification time, the memory usage and the power consumption. This paper proposes a new approach to improve the real-time behavior of a random forest classifier. This is accomplished by reducing the number of evaluated nodes and branches as well as by reducing the branch length in the underlying binary decision trees with numerical split values. A hardware architecture is presented for the improved tree-based classification method. A proof-of-concept implementation on an FPGA platform and some preliminary results show the advantage of this approach compared to related work.

Original languageBritish English
Title of host publication2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728133201
StatePublished - 2020
Event52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Virtual, Online
Duration: 10 Oct 202021 Oct 2020

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2020-October
ISSN (Print)0271-4310

Conference

Conference52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020
CityVirtual, Online
Period10/10/2021/10/20

Fingerprint

Dive into the research topics of 'Optimized random forest classifier for drone pilot identification'. Together they form a unique fingerprint.

Cite this