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Advances in Intelligent Data Analysis XIX

19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26–28, 2021, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Pedro Henriques Abreu

    1. University of Coimbra, Coimbra, Portugal

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  2. Pedro Pereira Rodrigues

    1. University of Porto, Porto, Portugal

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  3. Alberto Fernández

    1. University of Granada, Granada, Spain

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  4. João Gama

    1. University of Porto, Porto, Portugal

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12695)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

Included in the following conference series:

Conference proceedings info: IDA 2021.

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Table of contents (35 papers)

  1. Front Matter

    Pages i-xvi
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  2. Modeling with Neural Networks

    1. Front Matter

      Pages 1-1
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    2. Hyperspherical Weight Uncertainty in Neural Networks

      • Biraja Ghoshal, Allan Tucker
      Pages 3-11

    3. Partially Monotonic Learning for Neural Networks

      • Joana Trindade, João Vinagre, Kelwin Fernandes, Nuno Paiva, Alípio Jorge
      Pages 12-23

    4. Multiple-manifold Generation with an Ensemble GAN and Learned Noise Prior

      • Matthew Amodio, Smita Krishnaswamy
      Pages 24-36

    5. Simple, Efficient and Convenient Decentralized Multi-task Learning for Neural Networks

      • Amaury Bouchra Pilet, Davide Frey, Francois Taïani
      Pages 37-49

    6. Deep Hybrid Neural Networks with Improved Weighted Word Embeddings for Sentiment Analysis

      • Rania Othman, Rim Faiz, Youcef Abdelsadek, Kamel Chelghoum, Imed Kacem
      Pages 50-62

    7. Explaining Neural Networks by Decoding Layer Activations

      • Johannes Schneider, Michalis Vlachos
      Pages 63-75

    8. Analogical Embedding for Analogy-Based Learning to Rank

      • Mohsen Ahmadi Fahandar, Eyke Hüllermeier
      Pages 76-88

    9. HORUS-NER: A Multimodal Named Entity Recognition Framework for Noisy Data

      • Diego Esteves, José Marcelino, Piyush Chawla, Asja Fischer, Jens Lehmann
      Pages 89-100

  3. Modeling with Statistical Learning

    1. Front Matter

      Pages 101-101
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    2. Incremental Search Space Construction for Machine Learning Pipeline Synthesis

      • Marc-André Zöller, Tien-Dung Nguyen, Marco F. Huber
      Pages 103-115

    3. Adversarial Vulnerability of Active Transfer Learning

      • Nicolas M. Müller, Konstantin Böttinger
      Pages 116-127

    4. Revisiting Non-specific Syndromic Surveillance

      • Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz
      Pages 128-140

    5. Gradient Ascent for Best Response Regression

      • Victoria Racher, Christian Borgelt
      Pages 141-154

    6. Intelligent Structural Damage Detection: A Federated Learning Approach

      • Ali Anaissi, Basem Suleiman, Mohamad Naji
      Pages 155-170

    7. Composite Surrogate for Likelihood-Free Bayesian Optimisation in High-Dimensional Settings of Activity-Based Transportation Models

      • Vladimir Kuzmanovski, Jaakko Hollmén
      Pages 171-183

    8. Active Selection of Classification Features

      • Thomas T. Kok, Rachel M. Brouwer, Rene M. Mandl, Hugo G. Schnack, Georg Krempl
      Pages 184-195

    9. Feature Selection for Hierarchical Multi-label Classification

      • Luan V. M. da Silva, Ricardo Cerri
      Pages 196-208

    10. Bandit Algorithm for both Unknown Best Position and Best Item Display on Web Pages

      • Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont
      Pages 209-221
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Other volumes

  1. Advances in Intelligent Data Analysis XIX

Keywords

About this book

This book constitutes the proceedings of the 19th International Symposium on Intelligent Data Analysis, IDA 2021, which was planned to take place in Porto, Portugal. Due to the COVID-19 pandemic the conference was held online during April 26-28, 2021.

The 35 papers included in this book were carefully reviewed and selected from 113 submissions. The papers were organized in topical sections named: modeling with neural networks; modeling with statistical learning; modeling language and graphs; and modeling special data formats.

Editors and Affiliations

  • University of Coimbra, Coimbra, Portugal

    Pedro Henriques Abreu

  • University of Porto, Porto, Portugal

    Pedro Pereira Rodrigues, João Gama

  • University of Granada, Granada, Spain

    Alberto Fernández

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