| 研究課題/領域番号 |
22K17961
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分61030:知能情報学関連
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| 研究機関 | 国立情報学研究所 (2023)
国立研究開発法人産業技術総合研究所 (2022) |
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研究代表者 |
Ouyang Tinghui 国立情報学研究所, 情報社会相関研究系, 特任研究員 (80870849)
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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
3,770千円 (直接経費: 2,900千円、間接経費: 870千円)
2024年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2023年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
2022年度: 1,560千円 (直接経費: 1,200千円、間接経費: 360千円)
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| キーワード | Anomaly detection / Out-of-distribution / data structure / explaination / granular computing / data mining / anomaly data detection / information graph / structure description / explanation / graph neural network / anomaly detection / explainability / big data analysis |
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| 研究開始時の研究の概要 |
Aim at challenges of anomaly detection study related to big data and deep learning, an advanced graph neural network model is proposed. This research granulates big data in modeling to reduce computation cost, and leverage graph structure to provide good explainability for DL-based AD model.
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| 研究実績の概要 |
In terms of explanation of anomaly detection, we proposed granule-based anomalous data descriptor and detector for explanation. Moreover, we developed a structure matrix which is useful to realize data structure contraction and helpful to explain that anomaly data usually have large distance in the process of data structure contraction. In terms of anomaly detection applications, we apply the proposed granular AD detector to detect the out-of-distribution data in image and textual data. Moreover, a data quality assurance issue is discussed based on GPT-based sentiment analysis application.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
According to the proposal, our current progress is going as expected without delays. There are totally four conference papers and one journal submitted, among which two have been published in this fiscal year. In the next step, we will develop more anomaly detection algorithms and applications based on granular structure and granular information graph.
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| 今後の研究の推進方策 |
According to the plan in the proposal, we will do more research on developing advanced anomaly data detection algorithms and applications. One is to construct granular information graph, to develop GNN for anomaly detection, and to provide explanation. Then, we will continue solving various practical problems and applications related to anomaly data detection, and provide explanation for the AD process.
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