Unsupervised Outlier Detection based on Random Projection Outlyingness with Local Score Weighting
Summary :
This paper proposes an enhanced model of Random Projection Outlyingness (RPO) for unsupervised outlier detection. When datasets have multiple modalities, the RPOs have frequent detection errors. The proposed model deals with this problem via unsupervised clustering and a local score weighting. The experimental results demonstrate that the proposed model outperforms RPO and is comparable with other existing unsupervised models on benchmark datasets, in terms of in terms of Area Under the Curves (AUCs) of Receiver Operating Characteristic (ROC).
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.7 pp.1244-1248
- Publication Date
- 2023/07/01
- Publicized
- 2023/03/29
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDL8039
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Akira TAMAMORI
Aichi Institute of Technology
Keyword
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Akira TAMAMORI, "Unsupervised Outlier Detection based on Random Projection Outlyingness with Local Score Weighting" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 7, pp. 1244-1248, July 2023, doi: 10.1587/transinf.2022EDL8039.
Abstract: This paper proposes an enhanced model of Random Projection Outlyingness (RPO) for unsupervised outlier detection. When datasets have multiple modalities, the RPOs have frequent detection errors. The proposed model deals with this problem via unsupervised clustering and a local score weighting. The experimental results demonstrate that the proposed model outperforms RPO and is comparable with other existing unsupervised models on benchmark datasets, in terms of in terms of Area Under the Curves (AUCs) of Receiver Operating Characteristic (ROC).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8039/_p
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@ARTICLE{e106-d_7_1244,
author={Akira TAMAMORI, },
journal={IEICE TRANSACTIONS on Information},
title={Unsupervised Outlier Detection based on Random Projection Outlyingness with Local Score Weighting},
year={2023},
volume={E106-D},
number={7},
pages={1244-1248},
abstract={This paper proposes an enhanced model of Random Projection Outlyingness (RPO) for unsupervised outlier detection. When datasets have multiple modalities, the RPOs have frequent detection errors. The proposed model deals with this problem via unsupervised clustering and a local score weighting. The experimental results demonstrate that the proposed model outperforms RPO and is comparable with other existing unsupervised models on benchmark datasets, in terms of in terms of Area Under the Curves (AUCs) of Receiver Operating Characteristic (ROC).},
keywords={},
doi={10.1587/transinf.2022EDL8039},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Unsupervised Outlier Detection based on Random Projection Outlyingness with Local Score Weighting
T2 - IEICE TRANSACTIONS on Information
SP - 1244
EP - 1248
AU - Akira TAMAMORI
PY - 2023
DO - 10.1587/transinf.2022EDL8039
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2023
AB - This paper proposes an enhanced model of Random Projection Outlyingness (RPO) for unsupervised outlier detection. When datasets have multiple modalities, the RPOs have frequent detection errors. The proposed model deals with this problem via unsupervised clustering and a local score weighting. The experimental results demonstrate that the proposed model outperforms RPO and is comparable with other existing unsupervised models on benchmark datasets, in terms of in terms of Area Under the Curves (AUCs) of Receiver Operating Characteristic (ROC).
ER -
English
