Data Science and Machine Learning [electronic resource] : 21st Australasian Conference, AusDM 2023, Auckland, New Zealand, December 11-13, 2023, Proceedings / edited by Diana Benavides-Prado, Sarah Erfani, Philippe Fournier-Viger, Yee Ling Boo, Yun Sing Koh.

Colaborador(es): Benavides-Prado, Diana [editor.] | Erfani, Sarah [editor.] | Fournier-Viger, Philippe [editor.] | Boo, Yee Ling [editor.] | Koh, Yun Sing [editor.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Communications in Computer and Information Science ; 1943Editor: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: XII, 300 p. 94 illus., 83 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789819986965Tema(s): Data mining | Artificial intelligence | Image processing -- Digital techniques | Computer vision | Education -- Data processing | Computer science -- Mathematics | Data Mining and Knowledge Discovery | Artificial Intelligence | Computer Imaging, Vision, Pattern Recognition and Graphics | Computers and Education | Mathematics of ComputingFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.312 Clasificación LoC:QA76.9.D343Recursos en línea: Libro electrónicoTexto
Contenidos:
Research Track: Random Padding Data Augmentation -- Unsupervised Fraud Detection on Sparse Rating Networks -- Semi-Supervised Model-Based Clustering for Ordinal Data -- Damage GAN: A Generative Model for Imbalanced Data -- Text-Conditioned Graph Generation Using Discrete Graph Variational Autoencoders -- Boosting QA Performance through SA-Net and AA-Net with the Read+Verify Framework -- Anomaly Detection Algorithms: Comparative Analysis and Explainability Perspectives -- Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes -- MStoCast: Multimodal Deep Network for Stock Market Forecast. -- Few Shot and Transfer Learning with Manifold Distributed Datasets -- Mitigating The Adverse Effects of Long-tailed Data on Deep Learning Models -- Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression -- Hybrid Models for Predicting Cryptocurrency Price Using Financial and Non-Financial Indicators -- Application Track: Multi-Dimensional Data Visualization for Analyzing Materials -- Law in Order: An Open Legal Citation Network for New Zealand -- Enhancing Resource Allocation in IT Projects: The Potentials of Deep Learning-Based Recommendation Systems and Data-Driven Approaches -- A Comparison of One-Class versus Two-Class Machine Learning Models for Wildfire Prediction in California -- Skin Cancer Detection with Multimodal Data: A Feature Selection Approach Using Genetic Programming -- Comparison of Interpolation Techniques for Prolonged Exposure Estimation: A Case Study on Seven years of Daily Nitrogen Oxide in Greater Sydney -- Detecting Asthma Presentations from Emergency Department Notes: An Active Learning Approach.
En: Springer Nature eBookResumen: 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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Research Track: Random Padding Data Augmentation -- Unsupervised Fraud Detection on Sparse Rating Networks -- Semi-Supervised Model-Based Clustering for Ordinal Data -- Damage GAN: A Generative Model for Imbalanced Data -- Text-Conditioned Graph Generation Using Discrete Graph Variational Autoencoders -- Boosting QA Performance through SA-Net and AA-Net with the Read+Verify Framework -- Anomaly Detection Algorithms: Comparative Analysis and Explainability Perspectives -- Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes -- MStoCast: Multimodal Deep Network for Stock Market Forecast. -- Few Shot and Transfer Learning with Manifold Distributed Datasets -- Mitigating The Adverse Effects of Long-tailed Data on Deep Learning Models -- Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression -- Hybrid Models for Predicting Cryptocurrency Price Using Financial and Non-Financial Indicators -- Application Track: Multi-Dimensional Data Visualization for Analyzing Materials -- Law in Order: An Open Legal Citation Network for New Zealand -- Enhancing Resource Allocation in IT Projects: The Potentials of Deep Learning-Based Recommendation Systems and Data-Driven Approaches -- A Comparison of One-Class versus Two-Class Machine Learning Models for Wildfire Prediction in California -- Skin Cancer Detection with Multimodal Data: A Feature Selection Approach Using Genetic Programming -- Comparison of Interpolation Techniques for Prolonged Exposure Estimation: A Case Study on Seven years of Daily Nitrogen Oxide in Greater Sydney -- Detecting Asthma Presentations from Emergency Department Notes: An Active Learning Approach.

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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