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Advances in Knowledge Discovery and Data Mining

27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part I

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Hisashi Kashima

    1. Kyoto University, Kyoto, Japan

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  2. Tsuyoshi Ide

    1. IBM Research, Thomas J. Watson Research Center, Yorktown Heights, USA

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  3. Wen-Chih Peng

    1. National Chiao Tung University, Hsinchu, Taiwan

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13935)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: PAKDD 2023.

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Table of contents (43 papers)

  1. Front Matter

    Pages i-xvii
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  2. Anomaly and Outlier Detection

    1. Front Matter

      Pages 1-1
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    2. BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability

      • Xinglong Chang, Katharina Dost, Kaiqi Zhao, Ambra Demontis, Fabio Roli, Gillian Dobbie et al.
      Pages 3-14

    3. Fast and Attributed Change Detection on Dynamic Graphs with Density of States

      • Shenyang Huang, Jacob Danovitch, Guillaume Rabusseau, Reihaneh Rabbany
      Pages 15-26

    4. Outlying Aspect Mining via Sum-Product Networks

      • Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
      Pages 27-38

    5. TSI-GAN: Unsupervised Time Series Anomaly Detection Using Convolutional Cycle-Consistent Generative Adversarial Networks

      • Shyam Sundar Saravanan, Tie Luo, Mao Van Ngo
      Pages 39-54

    6. Achieving Counterfactual Fairness for Anomaly Detection

      • Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
      Pages 55-66

    7. The Common-Neighbors Metric Is Noise-Robust and Reveals Substructures of Real-World Networks

      • Sarel Cohen, Philipp Fischbeck, Tobias Friedrich, Martin Krejca
      Pages 67-79

    8. An Effective WGAN-Based Anomaly Detection Model for IoT Multivariate Time Series

      • Sibo Qi, Juan Chen, Peng Chen, Peian Wen, Wenyu Shan, Ling Xiong
      Pages 80-91

  3. Association Rules

    1. Front Matter

      Pages 93-93
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    2. RL-Net: Interpretable Rule Learning with Neural Networks

      • Lucile Dierckx, Rosana Veroneze, Siegfried Nijssen
      Pages 95-107

  4. Classification

    1. Front Matter

      Pages 109-109
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    2. The Causal Strength Bank: A New Benchmark for Causal Strength Classification

      • Xiaosong Yuan, Renchu Guan, Wanli Zuo, Yijia Zhang
      Pages 111-122

    3. Topological Graph Convolutional Networks Solutions for Power Distribution Grid Planning

      • Yuzhou Chen, Miguel Heleno, Alexandre Moreira, Yulia R. Gel
      Pages 123-134

    4. Label Distribution Learning with Discriminative Instance Mapping

      • Heng-Ru Zhang, Run-Ting Bai, Wen-Tao Tang
      Pages 135-146

    5. Leveraging Generative Models for Combating Adversarial Attacks on Tabular Datasets

      • Jiahui Zhou, Nayyar Zaidi, Yishuo Zhang, Paul Montague, Junae Kim, Gang Li
      Pages 147-158

    6. Weak Correlation-Based Discriminative Dictionary Learning for Image Classification

      • Huang-Kai Zhang, Li Zhang
      Pages 159-170

    7. Data-dependent and Scale-Invariant Kernel for Support Vector Machine Classification

      • Vinayaka Vivekananda Malgi, Sunil Aryal, Zafaryab Rasool, David Tay
      Pages 171-182

    8. Enhancing Robustness of Prototype with Attentive Information Guided Alignment in Few-Shot Classification

      • Tae-Hyung Kim, Woo-Jeoung Nam, Seong-Whan Lee
      Pages 183-194

  5. Clustering

    1. Front Matter

      Pages 195-195
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Other volumes

  1. Advances in Knowledge Discovery and Data Mining

  2. Advances in Knowledge Discovery and Data Mining

  3. Advances in Knowledge Discovery and Data Mining

  4. Advances in Knowledge Discovery and Data Mining

Keywords

About this book

The 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023.

The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.

Editors and Affiliations

  • Kyoto University, Kyoto, Japan

    Hisashi Kashima

  • IBM Research, Thomas J. Watson Research Center, Yorktown Heights, USA

    Tsuyoshi Ide

  • National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

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