Anomaly Detection Support for Crowdworkers by Providing Anomaly Scores

IF 0.3 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC IEICE Communications Express Pub Date : 2024-11-12 DOI:10.23919/comex.2024XBL0163
Tatsuki Tamano;Ryuya Itano;Honoka Tanitsu;Takahiro Koita
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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.
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通过提供异常分数为众工提供异常检测支持
近年来,罪案清除率一直很低。为了改进它们,目前的方法使用深度学习和众包来自动检测犯罪。然而,这些方法存在假阳性和假阴性的问题,也不能达到较高的准确性(AUC,正确答案率)。本文提出了一种方法,通过向众包工作者提供深度学习模型的异常得分输出来支持他们的判断。该方法提高了众包工作者做出正确判断的次数。结果,降低了假阳性和假阴性率,提高了准确率。
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来源期刊
IEICE Communications Express
IEICE Communications Express ENGINEERING, ELECTRICAL & ELECTRONIC-
自引率
33.30%
发文量
114
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