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Advances in Intelligent Data Analysis XXII

22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part II

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Ioanna Miliou

    1. Stockholm University, Kista, Sweden

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  2. Nico Piatkowski

    1. Fraunhofer IAIS, Sankt Augustin, Germany

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  3. Panagiotis Papapetrou

    1. Stockholm University, Kista, Sweden

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14642)

Included in the following conference series:

Conference proceedings info: IDA 2024.

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About this book

The two volume set LNCS 14641 and 14642 constitutes the proceedings of the 22nd International Symposium on Intelligent Data Analysis, IDA 2024, which was held in Stockholm, Sweden, during April 24-26, 2024.

The 40 full and 3 short papers included in the proceedings were carefully reviewed and selected from 94 submissions. IDA is an international symposium presenting advances in the intelligent analysis of data. Distinguishing characteristics of IDA are its focus on novel, inspiring ideas, its focus on research, and its relatively small scale. 

Keywords

Table of contents (22 papers)

  1. Front Matter

    Pages ii-xvi
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  2. Temporal and Sequence Data

    1. Front Matter

      Pages 1-1
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    2. Kernel Corrector LSTM

      • Rodrigo Tuna, Yassine Baghoussi, Carlos Soares, João Mendes-Moreira
      Pages 3-14

    3. Unsupervised Representation Learning for Smart Transportation

      • Thabang Lebese, Cécile Mattrand, David Clair, Jean-Marc Bourinet, François Deheeger
      Pages 15-27

    4. T-DANTE: Detecting Group Behaviour in Spatio-Temporal Trajectories Using Context Information

      • Maedeh Nasri, Thomas Maliappis, Carolien Rieffe, Mitra Baratchi
      Pages 28-39

  3. Statistical Learning

    1. Front Matter

      Pages 41-41
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    2. Backward Inference in Probabilistic Regressor Chains with Distributional Constraints

      • Ekaterina Antonenko, Michael Mechenich, Rita Beigaitė, Indrė Žliobaitė, Jesse Read
      Pages 43-55

    3. Empirical Comparison Between Cross-Validation and Mutation-Validation in Model Selection

      • Jinyang Yu, Sami Hamdan, Leonard Sasse, Abigail Morrison, Kaustubh R. Patil
      Pages 56-67

    4. Amplified Contribution Analysis for Federated Learning

      • Maciej Krzysztof Zuziak, Salvatore Rinzivillo
      Pages 68-79

  4. Data Mining

    1. Front Matter

      Pages 81-81
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    2. Monitoring Concept Drift in Continuous Federated Learning Platforms

      • Christoph Düsing, Philipp Cimiano
      Pages 83-94

    3. S+t-SNE - Bringing Dimensionality Reduction to Data Streams

      • Pedro C. Vieira, João P. Montrezol, João T. Vieira, João Gama
      Pages 95-106

    4. \(\lambda \)-DBSCAN: Augmenting DBSCAN with Prior Knowledge

      • Joel Dierkes, Daniel Stelter, Christian Braune
      Pages 107-118

    5. Putting Sense into Incomplete Heterogeneous Data with Hypergraph Clustering Analysis

      • Vishnu Manasa Devagiri, Pierre Dagnely, Veselka Boeva, Elena Tsiporkova
      Pages 119-130

  5. Optimization

    1. Front Matter

      Pages 131-131
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    2. Efficient Lookahead Decision Trees

      • Harold Kiossou, Pierre Schaus, Siegfried Nijssen, Gaël Aglin
      Pages 133-144

    3. Learning Curve Extrapolation Methods Across Extrapolation Settings

      • Lionel Kielhöfer, Felix Mohr, Jan N. van Rijn
      Pages 145-157

    4. Efficient NAS with FaDE on Hierarchical Spaces

      • Simon Neumeyer, Julian Stier, Michael Granitzer
      Pages 158-170

    5. Investigating the Relation Between Problem Hardness and QUBO Properties

      • Thore Gerlach, Sascha Mücke
      Pages 171-182

  6. XAI

    1. Front Matter

      Pages 183-183
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Other volumes

  1. Advances in Intelligent Data Analysis XXII

  2. Advances in Intelligent Data Analysis XXII

Editors and Affiliations

  • Stockholm University, Kista, Sweden

    Ioanna Miliou, Panagiotis Papapetrou

  • Fraunhofer IAIS, Sankt Augustin, Germany

    Nico Piatkowski

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