Outlier Detection with Explanations on Music Streaming Data: A Case Study with Danmark Music Group Ltd.

Autor: Jonas Herskind Sejr, Thorbjørn Christiansen, Nicolai Dvinge, Dan Hougesen, Peter Schneider-Kamp, Arthur Zimek

Publikováno v: Applied Sciences, Vol 11, Iss 5, p 2270 (2021)

In the digital marketplaces, businesses can micro-monitor sales worldwide and in real-time. Due to the vast amounts of data, there is a pressing need for tools that automatically highlight changing trends and anomalous (outlier) behavior that is pote

Externí odkaz: https://doaj.org/article/45958923a15d45669eb6f41c1c8f27e6

An interpretable measure of dataset complexity for imbalanced classification problems

Autor: Jonatan Møller Nuutinen Gøttcke, Colin Bellinger, Paula Branco, Arthur Zimek

Publikováno v: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) ISBN: 9781611977653

The class imbalance problem is associated with harmful classification bias and presents itself in a wide variety of important applications of supervised machine learning. Measures have been developed to determine the imbalance complexity of datasets

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::086629ea92598d02f3d5ddad52c56aec
https://doi.org/10.1137/1.9781611977653.ch29

A Simple Meta-path-free Framework for Heterogeneous Network Embedding

Autor: Rui Zhang, Peter Schneider-Kamp, Arthur Zimek

Publikováno v: Proceedings of the 31st ACM International Conference on Information & Knowledge Management.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::add7bade025147f729a18c10fb98f414
https://doi.org/10.1145/3511808.3557223

Power of Explanations: Towards automatic debiasing in hate speech detection

Autor: Yi Cai, Arthur Zimek, Gerhard Wunder, Eirini Ntoutsi

Publikováno v: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA).

Hate speech detection is a common downstream application of natural language processing (NLP) in the real world. In spite of the increasing accuracy, current data-driven approaches could easily learn biases from the imbalanced data distributions orig

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0622bcec6592c1fe0eaeaf521f8dfbfb
https://doi.org/10.1109/dsaa54385.2022.10032325

Internal Evaluation of Unsupervised Outlier Detection

Autor: Ricardo J. G. B. Campello, Jörg Sander, Arthur Zimek, Henrique O. Marques

Publikováno v: Marques, H O, Campello, R J G B, Sander, J & Zimek, A 2020, ' Internal Evaluation of Unsupervised Outlier Detection ', ACM Transactions on Knowledge Discovery from Data, vol. 14, no. 4, 47 . https://doi.org/10.1145/3394053
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP

Although there is a large and growing literature that tackles the unsupervised outlier detection problem, the unsupervised evaluation of outlier detection results is still virtually untouched in the literature. The so-called internal evaluation, base

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0b82752d0a1ba71b9c881fc08ef5544
https://doi.org/10.1145/3394053