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篇名 |
Effective Classification for Multi-modal Behavioral Authentication on Large-Scale Data
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並列篇名 | Effective Classification for Multi-modal Behavioral Authentication on Large-Scale Data |
作者 | Shuji Yamaguchi、Hidehito Gomi、Ryosuke Kobayashi、Rie Shigetomi Yamaguchi |
英文摘要 | We propose an effective classification algorithm for machine learning to achieve higher performance for multi-modal behavioral authentication systems. Our algorithm uses a multiclass classification scheme that has a smaller number of classes than the number of users stored in the dataset. We also propose metrics, the selfmix- classified rate, other-single-classified rate, and equal-classified rate, for use with the proposed algorithm to determine an optimal number of classes for behavioral authentication. We conducted experiments using a largescale dataset of activity histories that are stored when 100,000 users use commercial smartphone-applications to analyze performance measures such as false rejection rate, false acceptance rate, and equal error rate obtained with our proposed algorithm. The results indicate our algorithm achieved higher performance than that for previous ones. |
起訖頁 | 1169-1181 |
關鍵詞 | Behavioral authentication、Personal data analysis、Smartphone application、Big data |
刊名 | 網際網路技術學刊 |
期數 | 202109 (22:5期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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