Search Results for author: Kerstin Bunte

Found 7 papers, 2 papers with code

An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data

no code implementations12 Jun 2022 Michiel Straat, Kevin Koster, Nick Goet, Kerstin Bunte

The model achieves an excellent performance (F3-score of 0. 95) predicting material running out of specifications for the tensile strength.

Fault Detection

Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets

no code implementations4 Jun 2022 Sreejita Ghosh, Elizabeth S. Baranowski, Michael Biehl, Wiebke Arlt, Peter Tino, Kerstin Bunte

Medical datasets face common issues such as heterogeneous measurements, imbalanced classes with limited sample size, and missing data, which hinder the straightforward application of machine learning techniques.

Interpretable Machine Learning

Secure Formation Control via Edge Computing Enabled by Fully Homomorphic Encryption and Mixed Uniform-Logarithmic Quantization

no code implementations13 Apr 2022 Matteo Marcantoni, Bayu Jayawardhana, Mariano Perez Chaher, Kerstin Bunte

Recent developments in communication technologies, such as 5G, together with innovative computing paradigms, such as edge computing, provide further possibilities for the implementation of real-time networked control systems.

Edge-computing Quantization

Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques

1 code implementation5 Jan 2022 Mohammad Mohammadi, Jarvin Mutatiina, Teymoor Saifollahi, Kerstin Bunte

Both methods are able to identify UCDs/GCs with a precision and a recall of >93 percent and provide relevances that reflect the importance of each feature dimension %(colors and angular sizes) for the classification.

Feature Importance Quantization

LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density

1 code implementation17 Sep 2020 Abolfazl Taghribi, Kerstin Bunte, Rory Smith, Jihye Shin, Michele Mastropietro, Reynier F. Peletier, Peter Tino

The algorithm performance in comparison to state-of-the-art approaches for noise reduction in manifold detection and clustering is demonstrated, on several synthetic and real datasets, including an N-body simulation of a cosmological volume.

Dimensionality Reduction

Visualisation and knowledge discovery from interpretable models

no code implementations7 May 2020 Sreejita Ghosh, Peter Tino, Kerstin Bunte

In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem.

Decision Making Fairness +1

Sparse group factor analysis for biclustering of multiple data sources

no code implementations29 Dec 2015 Kerstin Bunte, Eemeli Leppäaho, Inka Saarinen, Samuel Kaski

Motivation: Modelling methods that find structure in data are necessary with the current large volumes of genomic data, and there have been various efforts to find subsets of genes exhibiting consistent patterns over subsets of treatments.

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