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Data Science and Machine Learning

21st Australasian Conference, AusDM 2023, Auckland, New Zealand, December 11–13, 2023, Proceedings

  • Conference proceedings
  • © 2024

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Editors:
  1. Diana Benavides-Prado

    1. The University of Auckland, Auckland, New Zealand

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  2. Sarah Erfani

    1. The University of Melbourne, Carlton, Australia

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  3. Philippe Fournier-Viger

    1. Shenzhen University, Shenzhen, China

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  4. Yee Ling Boo

    1. RMIT University, Melbourne, Australia

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  5. Yun Sing Koh

    1. The University of Auckland, Auckland, New Zealand

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Part of the book series: Communications in Computer and Information Science (CCIS, volume 1943)

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Conference proceedings info: AusDM 2023.

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About this book

This book constitutes the proceedings of the 21st Australasian Conference on Data Science and Machine Learning, AusDM 2023, held in Auckland, New Zealand, during December 11–13, 2023.


The 20 full papers presented in this book were carefully reviewed and selected from 50 submissions. The papers are organized in the following topical sections: research track and application track. They deal with topics around data science and machine learning in everyday life. 

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Table of contents (20 papers)

  1. Front Matter

    Pages i-xii
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  2. Research Track

    1. Front Matter

      Pages 1-1
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    2. Random Padding Data Augmentation

      • Nan Yang, Laicheng Zhong, Fan Huang, Wei Bao, Dong Yuan
      Pages 3-18

    3. Unsupervised Fraud Detection on Sparse Rating Networks

      • Shaowen Tang, Raymond Wong
      Pages 19-33

    4. Semi-supervised Model-Based Clustering for Ordinal Data

      • Ying Cui, Louise McMillan, Ivy Liu
      Pages 34-47

    5. Damage GAN: A Generative Model for Imbalanced Data

      • Ali Anaissi, Yuanzhe Jia, Ali Braytee, Mohamad Naji, Widad Alyassine
      Pages 48-61

    6. Text-Conditioned Graph Generation Using Discrete Graph Variational Autoencoders

      • Michael Longland, David Liebowitz, Kristen Moore, Salil Kanhere
      Pages 62-74

    7. Boosting QA Performance Through SA-Net and AA-Net with the Read+Verify Framework

      • Liang Tang, Qianqian Qi, Qinghua Shang, Yuguang Cai, Jiamou Liu, Michael Witbrock et al.
      Pages 75-89

    8. Anomaly Detection Algorithms: Comparative Analysis and Explainability Perspectives

      • Sadeq Darrab, Harshitha Allipilli, Sana Ghani, Harikrishnan Changaramkulath, Sricharan Koneru, David Broneske et al.
      Pages 90-104

    9. Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes

      • Manh Khoi Duong, Stefan Conrad
      Pages 105-120

    10. MStoCast: Multimodal Deep Network for Stock Market Forecast

      • Kamaladdin Fataliyev, Wei Liu
      Pages 121-136

    11. Few-Shot and Transfer Learning with Manifold Distributed Datasets

      • Sayed Waleed Qayyumi, Laurence A. F. Park, Oliver Obst
      Pages 137-149

    12. Mitigating the Adverse Effects of Long-Tailed Data on Deep Learning Models

      • Din Muhammad Sangrasi, Lei Wang, Markus Hagenbuchner, Peng Wang
      Pages 150-162

    13. Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression

      • Chunyu Wang, Qi Chen, Bing Xue, Mengjie Zhang
      Pages 163-176

    14. Hybrid Models for Predicting Cryptocurrency Price Using Financial and Non-Financial Indicators

      • Tulika Shrivastava, Basem Suleiman, Muhammad Johan Alibasa
      Pages 177-191

  3. Application Track

    1. Front Matter

      Pages 193-193
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    2. Multi-dimensional Data Visualization for Analyzing Materials

      • Amit Vurgaft
      Pages 195-210

    3. Law in Order: An Open Legal Citation Network for New Zealand

      • Tobias Milz, Elizabeth Macpherson, Varvara Vetrova
      Pages 211-225

    4. Enhancing Resource Allocation in IT Projects: The Potentials of Deep Learning-Based Recommendation Systems and Data-Driven Approaches

      • Li Xiao, Samaneh Madanian, Weihua Li, Yuchun Xiao
      Pages 226-238

    5. A Comparison of One-Class Versus Two-Class Machine Learning Models for Wildfire Prediction in California

      • Fathima Nuzla Ismail, Abira Sengupta, Brendon J. Woodford, Sherlock A. Licorish
      Pages 239-253
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Other volumes

  1. Data Science and Machine Learning

Editors and Affiliations

  • The University of Auckland, Auckland, New Zealand

    Diana Benavides-Prado, Yun Sing Koh

  • The University of Melbourne, Carlton, Australia

    Sarah Erfani

  • Shenzhen University, Shenzhen, China

    Philippe Fournier-Viger

  • RMIT University, Melbourne, Australia

    Yee Ling Boo

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