{"title":"通过提供异常分数为众工提供异常检测支持","authors":"Tatsuki Tamano;Ryuya Itano;Honoka Tanitsu;Takahiro Koita","doi":"10.23919/comex.2024XBL0163","DOIUrl":null,"url":null,"abstract":"In recent years, crime clearance rates have remained low. To improve them, current methods have used deep learning and crowdsourcing to automatically detect crimes. However, these methods suffer from false positives and false negatives and also fail to achieve high accuracy (AUC, correct answer rate). This paper proposes a method that supports the judgments of crowdworkers by providing them with anomaly-score output from deep learning models. The proposed method enhance the number of correct judgments made by crowdworkers. As a result, false positive and false negative rates are reduced and accuracy is improved.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"14 1","pages":"8-11"},"PeriodicalIF":0.3000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10751757","citationCount":"0","resultStr":"{\"title\":\"Anomaly Detection Support for Crowdworkers by Providing Anomaly Scores\",\"authors\":\"Tatsuki Tamano;Ryuya Itano;Honoka Tanitsu;Takahiro Koita\",\"doi\":\"10.23919/comex.2024XBL0163\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, crime clearance rates have remained low. To improve them, current methods have used deep learning and crowdsourcing to automatically detect crimes. However, these methods suffer from false positives and false negatives and also fail to achieve high accuracy (AUC, correct answer rate). This paper proposes a method that supports the judgments of crowdworkers by providing them with anomaly-score output from deep learning models. The proposed method enhance the number of correct judgments made by crowdworkers. As a result, false positive and false negative rates are reduced and accuracy is improved.\",\"PeriodicalId\":54101,\"journal\":{\"name\":\"IEICE Communications Express\",\"volume\":\"14 1\",\"pages\":\"8-11\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2024-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10751757\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEICE Communications Express\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10751757/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEICE Communications Express","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10751757/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Anomaly Detection Support for Crowdworkers by Providing Anomaly Scores
In recent years, crime clearance rates have remained low. To improve them, current methods have used deep learning and crowdsourcing to automatically detect crimes. However, these methods suffer from false positives and false negatives and also fail to achieve high accuracy (AUC, correct answer rate). This paper proposes a method that supports the judgments of crowdworkers by providing them with anomaly-score output from deep learning models. The proposed method enhance the number of correct judgments made by crowdworkers. As a result, false positive and false negative rates are reduced and accuracy is improved.