Publikationen: Martha Krakowski

Autor: Jahr:

2021

  ·M. Krakowski:
Detection of Anomalous Sequences in Multivariate Time Series Data

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2020

  ·G. Klassen, M. Krakowski, S. Conrad:
Clustering of Time Series Regarding Their Over-Time Stability
Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (Code available here: https://github.com/tatusch/ots-eval)

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  ·S. Conrad, M. Krakowski, K. Bogomasov, G. Klassen:
Anwendungsgebiete für die automatisierte Informationsgewinnung aus Bildern
Bilddaten in den Digitalen Geisteswissenschaften

 
  ·M. Krakowski, G. Klassen, S. Conrad:
Loners stand out. Identification of anomalous subsequences based on group performance
Advanced Data Mining and Applications, ADMA 2020 (Code available here: https://github.com/tatusch/ots-eval)

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  ·M. Krakowski, G. Klassen, S. Conrad:
Behave or be detected! Identifying outlier sequences by their group cohesion
Big Data Analytics and Knowledge Discovery, 22nd International Conference, DaWaK 2020 (Code available here: https://github.com/tatusch/ots-eval)

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  ·G. Klassen, M. Krakowski, W. Huo, S. Conrad:
Evaluating Machine Learning Algorithms in Predicting Financial Restatements
ICBIM '20: Proceedings of the 4th International Conference on Business and Information Management

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  ·M. Krakowski, G. Klassen, M. Bravidor, S. Conrad:
Predicting Erroneous Financial Statements Using a Density-Based Clustering Approach
ICBIM '20: Proceedings of the 4th International Conference on Business and Information Management

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  ·M. Krakowski, G. Klassen, M. Bravidor, S. Conrad:
How is Your Team Spirit? Cluster Over-Time Stability Evaluation
Machine Learning and Data Mining in Pattern Recognition, 16th International Conference on Machine Learning and Data Mining, MLDM 2020 (Code available here: https://github.com/tatusch/ots-eval)

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  ·G. Klassen, M. Krakowski, L. Himmelspach, S. Conrad:
Fuzzy Clustering Stability Evaluation of Time Series
Information Processing and Management of Uncertainty in Knowledge-Based Systems, 18th International Conference, IPMU 2020 (Code available here: https://github.com/tatusch/ots-eval)

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2019

  ·M. Krakowski, G. Klassen, M. Bravidor, S. Conrad:
Show Me Your Friends and I'll Tell You Who You Are. Finding Anomalous Time Series by Conspicuous Cluster Transitions
Data Mining. AusDM 2019. Communications in Computer and Information Science (Code available here: https://github.com/tatusch/ots-eval)

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2018

  ·K. Bogomasov, L. Himmelspach, G. Klassen, M. Krakowski, S. Conrad:
Feature-Based Approach for Severity Scoring of Lung Tuberculosis from CT Images
Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum

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  ·M. Krakowski, S. Conrad:
Detection of Multidrug-Resistant Tuberculosis Using Convolutional Neural Networks and Decision Trees
Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum

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  ·M. Krakowski:
Approaches for the Improvement of the Multilabel Multiclass Classification with a Huge Number of Classes
Proceedings of the 30th GI-Workshop Grundlagen von Datenbanken, Wuppertal, Germany, May 22-25, 2018

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2017

  ·D. Braun, M. Singhof, M. Krakowski, S. Conrad:
Convolutional Neural Networks for Multidrug-resistant and Drug-sensitive Tuberculosis Dinstinction
CLEF2017 Working Notes

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