Abstrakti
Smoothing Outlier Scores is All You Need to Improve Outlier Detectors (Extended Abstract)
Tekijät: Yang, Jiawei; Rahardja, Susanto; Fränti, Pasi
Toimittaja: N/A
Konferenssin vakiintunut nimi: International Conference on Data Engineering
Kustantaja: IEEE
Julkaisuvuosi: 2025
Kokoomateoksen nimi: 2025 IEEE 41st International Conference on Data Engineering (ICDE)
Aloitussivu: 4736
Lopetussivu: 4737
DOI: https://doi.org/10.1109/ICDE65448.2025.00397
Verkko-osoite: https://doi.org/10.1109/icde65448.2025.00397
Existing outlier detectors calculate outlier scores for data objects independently, ignoring the consistency between score similarity and object similarity. As a result, these detectors may produce inconsistent scores for similar objects, leading the scores of some normal objects to exceed some of outlier objects, increasing the possibility of misclassification. To address this issue, we first assume that similar objects should have similar scores. Then, based on this assumption, we propose neighborhood averaging, an outlier score post-processing technique to improve any single outlier detector, which is the first of its kind.
Julkaisussa olevat rahoitustiedot:
This work has been co-funded by the European Union's Horizon Europe Framework programme for research and in-novation 2021–2027 under the Marie Skłodowska-Curie grant agreement n° 101126611.