Ricardo Silva Carvalho, Rommel N. Carvalho, G. N. Ramos, R. Mourão
{"title":"预测银行柜员队列的等待时间溢出","authors":"Ricardo Silva Carvalho, Rommel N. Carvalho, G. N. Ramos, R. Mourão","doi":"10.1109/ICMLA.2017.00-51","DOIUrl":null,"url":null,"abstract":"This study proposes a predictive model to detect the delay in bank teller queues. Since there are penalties and fines applied to the branches that leave their clients waiting for a long time, detecting these cases as early as possible is essential. Four models were tested: one using a Queuing Theory's formula and the other three using Data Mining algorithms -- Deep Learning (DL), Gradient Boost Machine (GBM), and Random Forest (RF). The results indicated the GBM model as the most efficient, with an accuracy of 97% and a F1-measure of 75%.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"60 1","pages":"842-847"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Predicting Waiting Time Overflow on Bank Teller Queues\",\"authors\":\"Ricardo Silva Carvalho, Rommel N. Carvalho, G. N. Ramos, R. Mourão\",\"doi\":\"10.1109/ICMLA.2017.00-51\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study proposes a predictive model to detect the delay in bank teller queues. Since there are penalties and fines applied to the branches that leave their clients waiting for a long time, detecting these cases as early as possible is essential. Four models were tested: one using a Queuing Theory's formula and the other three using Data Mining algorithms -- Deep Learning (DL), Gradient Boost Machine (GBM), and Random Forest (RF). The results indicated the GBM model as the most efficient, with an accuracy of 97% and a F1-measure of 75%.\",\"PeriodicalId\":6636,\"journal\":{\"name\":\"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)\",\"volume\":\"60 1\",\"pages\":\"842-847\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLA.2017.00-51\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2017.00-51","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting Waiting Time Overflow on Bank Teller Queues
This study proposes a predictive model to detect the delay in bank teller queues. Since there are penalties and fines applied to the branches that leave their clients waiting for a long time, detecting these cases as early as possible is essential. Four models were tested: one using a Queuing Theory's formula and the other three using Data Mining algorithms -- Deep Learning (DL), Gradient Boost Machine (GBM), and Random Forest (RF). The results indicated the GBM model as the most efficient, with an accuracy of 97% and a F1-measure of 75%.
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