Search Results for author: Arthur Zimek

Found 9 papers, 5 papers with code

Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis

1 code implementation10 Jan 2024 Alastair Anderberg, James Bailey, Ricardo J. G. B. Campello, Michael E. Houle, Henrique O. Marques, Miloš Radovanović, Arthur Zimek

We present a nonparametric method for outlier detection that takes full account of local variations in intrinsic dimensionality within the dataset.

Outlier Detection

Explaining text classifiers through progressive neighborhood approximation with realistic samples

no code implementations11 Feb 2023 Yi Cai, Arthur Zimek, Eirini Ntoutsi, Gerhard Wunder

The importance of neighborhood construction in local explanation methods has been already highlighted in the literature.

Power of Explanations: Towards automatic debiasing in hate speech detection

1 code implementation7 Sep 2022 Yi Cai, Arthur Zimek, Gerhard Wunder, Eirini Ntoutsi

Hate speech detection is a common downstream application of natural language processing (NLP) in the real world.

Fairness Hate Speech Detection

Detecting Wandering Behavior of People with Dementia

no code implementations25 Oct 2021 Nicklas Sindlev Andersen, Marco Chiarandini, Stefan Jänicke, Panagiotis Tampakis, Arthur Zimek

Wandering is a problematic behavior in people with dementia that can lead to dangerous situations.

XPROAX-Local explanations for text classification with progressive neighborhood approximation

1 code implementation30 Sep 2021 Yi Cai, Arthur Zimek, Eirini Ntoutsi

The importance of the neighborhood for training a local surrogate model to approximate the local decision boundary of a black box classifier has been already highlighted in the literature.

counterfactual text-classification +1

Subspace Determination through Local Intrinsic Dimensional Decomposition: Theory and Experimentation

no code implementations15 Jul 2019 Ruben Becker, Imane Hafnaoui, Michael E. Houle, Pan Li, Arthur Zimek

For each point, the recently-proposed Local Intrinsic Dimension (LID) model is used in identifying the axis directions along which features have the greatest local discriminability, or equivalently, the fewest number of components of LID that capture the local complexity of the data.

Clustering

ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"

1 code implementation10 Feb 2019 Erich Schubert, Arthur Zimek

We will first outline the motivation for this release, the plans for the future, and then give a brief overview over the new functionality in this version.

Benchmarking Clustering +6

A Framework for Clustering Uncertain Data

1 code implementation VLDB 2015 Erich Schubert, Alexander Koos, Tobias Emrich, Andreas Zufle, Klaus Arthur Schmid, Arthur Zimek

The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community.

Clustering Outlier Detection

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