{"title":"Machine Learning Classifier-Based Metrics Can Evaluate the Efficiency of Separation Systems","authors":"Éva Kenyeres, Alex Kummer, János Abonyi","doi":"10.3390/e26070571","DOIUrl":null,"url":null,"abstract":"This paper highlights that metrics from the machine learning field (e.g., entropy and information gain) used to qualify a classifier model can be used to evaluate the effectiveness of separation systems. To evaluate the efficiency of separation systems and their operation units, entropy- and information gain-based metrics were developed. The receiver operating characteristic (ROC) curve is used to determine the optimal cut point in a separation system. The proposed metrics are verified by simulation experiments conducted on the stochastic model of a waste-sorting system. Machine learning classifier-based metrics has promising potential to gain information about the performance of separation systems. Industrial separation systems can be considered to perform a classification task. Initialized by this analogy, existing metrics from the machine learning field (e.g., entropy and information gain) to qualify a classifier can be used to evaluate the effectiveness of these systems. Our research investigates this idea generally, and also introduces a case study of an industrial manual waste-sorting system. The contributions of the paper are the following: (1) Overview of the possible applications of classifier-based metrics for process development aims. (2) Entropy and information gain are shown to be applicable to evaluate the efficiency of separation systems and their operation units as well. (3) Monte Carlo simulation is involved to produce robust results in a separation system with stochastic phenomena. (4) The ROC curve is shown to be applicable to determining the optimal cut point in a separation system. The ideas above are verified by simulation experiments conducted on the stochastic model of a waste-sorting system.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Entropy","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.3390/e26070571","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
This paper highlights that metrics from the machine learning field (e.g., entropy and information gain) used to qualify a classifier model can be used to evaluate the effectiveness of separation systems. To evaluate the efficiency of separation systems and their operation units, entropy- and information gain-based metrics were developed. The receiver operating characteristic (ROC) curve is used to determine the optimal cut point in a separation system. The proposed metrics are verified by simulation experiments conducted on the stochastic model of a waste-sorting system. Machine learning classifier-based metrics has promising potential to gain information about the performance of separation systems. Industrial separation systems can be considered to perform a classification task. Initialized by this analogy, existing metrics from the machine learning field (e.g., entropy and information gain) to qualify a classifier can be used to evaluate the effectiveness of these systems. Our research investigates this idea generally, and also introduces a case study of an industrial manual waste-sorting system. The contributions of the paper are the following: (1) Overview of the possible applications of classifier-based metrics for process development aims. (2) Entropy and information gain are shown to be applicable to evaluate the efficiency of separation systems and their operation units as well. (3) Monte Carlo simulation is involved to produce robust results in a separation system with stochastic phenomena. (4) The ROC curve is shown to be applicable to determining the optimal cut point in a separation system. The ideas above are verified by simulation experiments conducted on the stochastic model of a waste-sorting system.
期刊介绍:
Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.