Classifier Selection in Resource Limited Hardware: Decision Analysis and Resolution Approach

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Zeki Sistemler Teori ve Uygulamaları Dergisi
Volume:4 Issue:1
Classifier Selection in Resource Limited Hardware: Decision Analysis and Resolution Approach

Classifier Selection in Resource Limited Hardware: Decision Analysis and Resolution Approach

Pages : 37-42

View : 54 | Download : 10

Publication Date : 2021-03-24

Article Type : Research Paper

Abstract :Digitalization, Industry 4.0 and Internet of things insert ignore into journalissuearticles values(IoT); have become more popular in the recent years. Most of these systems depend on micro-controllers and sensors. These micro-controllers and sensors are mostly cheap, low RAM and low CPU systems; thus, they are resource constrained environments. In this study, a supervised learning classifier comparison technique suitable for resource constrained environments is proposed. This technique, Decision Analysis and Resolution insert ignore into journalissuearticles values(DAR);, is originated in the domain of Software Engineering. First, DAR is explained using an example of car buying scenario. Then 11 off-the-shelf classifiers are compared using DAR for low RAM and less powerful CPU environments in an intrusion detection scenario. This scenario simulated on well-known KDD99 intrusion detection dataset. All the experiments are realized using python scikit-learn package. According to the experiments, Decision Tree classifier is the most suitable to implement for resource constrained environments with a small lead. Results for the other three classifiers insert ignore into journalissuearticles values(Bagging, Multi Layer Perceptron, Random Forest); are also very similar. To aid the reproducibility of the experiments, the whole source code of the study is provided in the popular open source repository https://github.com/ati-ozgur/classifier-comparison-using-DAR.
Keywords : Classifier Selection, Decision Analysis and Resolution, Machine Learning, Performance Metrics, Resource Limited Environment

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