Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part IV / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin

• Autore: Yang De-Nian
• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (380 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Algorithms; Education - Data processing; Computer science - Mathematics; Signal processing; Computer networks; Artificial Intelligence; Design and Analysis of Algorithms; Computers and Education; Mathematics of Computing; Signal, Speech and Image Processing; Computer Communication Networks
• Altri autori: XieXing TsengVincent S PeiJian HuangJen-Wei LinJerry Chun-Wei
• Nota di contenuto: Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part IV -- Financial Data -- Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal -- 1 Introduction -- 2 Preliminaries -- 3 Approach -- 3.1 Graph Construction -- 3.2 Transaction Encoding -- 3.3 Neighbor Aggregator -- 3.4 Community Aggregator -- 3.5 Dynamic Adaptor -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Overall Performance -- 4.3 Ablation Study -- 5 Conclusion -- References -- Multi-time Window Ensemble and Maximization of Expected Return for Stock Movement Prediction -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 3.1 Problem Formulation -- 3.2 Multi-time Window Ensemble Classifier -- 3.3 Base Learner -- 3.4 Proposed Loss Function for Base Learner -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Predictive Performance Comparison -- 4.3 Trading Performance Comparison -- 4.4 Ablation Study -- 4.5 Visualization of Proposed Loss Function -- 5 Conclusion -- References -- MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading -- 1 Introduction -- 2 Problem Formulation -- 3 Methodology -- 3.1 Imitation Learning -- 3.2 Pretrain Module -- 3.3 Multiple Actors -- 3.4 Optimal Transport Regularization -- 4 Experiments -- 4.1 Dataset -- 4.2 Baselines, Evaluation Metrics and Hyperparameters -- 4.3 Experimental Results -- 4.4 Ablation Study -- 5 Related Work -- 6 Conclusion -- References -- Agent-Based Simulation of Decision-Making Under Uncertainty to Study Financial Precarity -- 1 Introduction -- 2 Background: Modeling Consumption -- 2.1 Capturing Uncertainty -- 3 The Framework: Introducing Real Constraints -- 3.1 Background: Modeling Ruin -- 3.2 Our New Model -- 4 Simulation Study: Precarity -- 4.1 Long Term Precarity -- 4.2 Factors Contributing to Precarity. ; 5 Simulation Study: Interventions -- 6 Related Work -- 7 Conclusions -- References -- Information Retrieval and Search -- Semantic Completion: Enhancing Image-Text Retrieval with Information Extraction and Compression -- 1 Introduction -- 2 Related Work -- 2.1 Dual-Stream Structure -- 2.2 Single-Stream Structure -- 3 Methodology -- 3.1 Overview -- 3.2 Information Extraction and Compression (IEC) -- 3.3 Training Tasks -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Comparison to Baseline -- 4.3 Ablation Study -- 5 Conclusion -- References -- Fast Edit Distance Prediction for All Pairs of Sequences in Very Large NGS Datasets -- 1 Introduction -- 2 Related Works -- 3 Basic Concepts -- 3.1 Preliminary -- 3.2 Key Ideas -- 3.3 Selecting the Reference Sequences -- 4 Predicting the Edit Distance -- 5 Edit Distance Prediction from Non-matching Sub-sequences -- 5.1 Differences of Distances with Respect to Pairs of Letters -- 5.2 Computational Complexity -- 6 Results and Discussions -- 6.1 Datasets -- 6.2 Selecting K-Mer Length -- 6.3 Comparison of the Proposed Strategies -- 6.4 Edit Distance Prediction -- 6.5 Hierarchical Clustering -- 7 Conclusion -- References -- MixCL: Mixed Contrastive Learning for Relation Extraction -- 1 Introduction -- 2 Mixed Contrastive Learning -- 2.1 Neural Relation Extraction Baseline Model -- 2.2 Entity Centralized Contrastive Learning for Relation Extraction -- 2.3 Mixed Contrastive Learning for Relation Extraction -- 3 Experiments -- 3.1 Datasets and Comparison Models -- 3.2 Main Results -- 4 Analysis -- 4.1 Metric of Quality of Negative Examples -- 4.2 Why Does MixCL Work? -- 5 Related Work -- 5.1 Neural Relation Extraction -- 5.2 Contrastive Learning -- 6 Conclusion -- References -- Decomposing Relational Triple Extraction with Large Language Models for Better Generalization on Unseen Data -- 1 Introduction. ; 2 Methodology -- 2.1 Problem Formalization -- 2.2 Sub-task 1: Relation Extraction -- 2.3 Sub-task 2: Entity Extraction -- 2.4 Sub-task 3: Triple Filtering -- 3 Experiments -- 3.1 Datasets -- 3.2 Compared Methods and Evaluation Metrics -- 3.3 Overall Performance -- 3.4 Model Generalization Evaluation -- 3.5 Ablation Studies -- 4 Related Work -- 5 Conclusion -- References -- Multi-Query Person Search with Transformers -- 1 Introduction -- 2 Method -- 2.1 Multi-Query Decoder -- 2.2 Detection and Identification Losses -- 3 Experiments -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Implementation Details -- 3.4 Comparison to the State-of-the-Art Approaches -- 3.5 Model Analysis -- 4 Conclusion -- References -- BioReX: Biomarker Information Extraction Inspired by Aspect-Based Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Biomarker Extraction (BE) -- 3.2 Result Association (RA) -- 4 Experiments -- 4.1 Experiment Setting -- 4.2 Results and Analysis -- 5 Conclusions -- References -- IR Embedding Fairness Inspection via Contrastive Learning and Human-AI Collaborative Intelligence -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Model Overview -- 3.2 GP Based Contrastive Learning -- 4 Experiments and Deployment -- 4.1 Implementation -- 4.2 Offline Evaluation -- 4.3 Online A/B Testing -- 5 Conclusion -- References -- SemPool: Simple, Robust, and Interpretable KG Pooling for Enhancing Language Models -- 1 Introduction -- 2 Related Work -- 3 Problem Statement and Preliminaries -- 4 Empirical Findings on Robustness -- 5 SemPool: Semantic Graph Pooling -- 5.1 KG Initialization -- 5.2 Pooling -- 5.3 KG Grounding -- 6 Experimental Setting -- 7 Results -- 7.1 Main Results -- 7.2 Ablation Studies and Analysis -- 8 Conclusions -- References -- Medical and Biological Data. ; Spatial Gene Expression Prediction Using Multi-Neighborhood Network with Reconstructing Attention -- 1 Introduction -- 2 Related Work -- 2.1 Gene Expression Prediction -- 2.2 Vision Transformer (ViT) -- 3 Method -- 3.1 Dual-Scale Attention -- 3.2 Reconstructing Attention -- 3.3 Transformer Block -- 4 Experiment -- 4.1 Datasets -- 4.2 Experiment Setup -- 5 Results and Discussion -- 5.1 Baseline Experiments for Multi-Neighborhood Network -- 5.2 Influence of Number of Neighborhoods -- 5.3 Influence of Attention Mechanisms -- 6 Conclusion -- References -- APFL: Active-Passive Forgery Localization for Medical Images -- 1 Introduction -- 2 Related Work -- 2.1 Active Forgery Localization -- 2.2 Passive Forgery Localization -- 3 Methodology -- 3.1 Active Fuzzy Localization with Reversible Watermarking -- 3.2 Passive Precise Localization with Lightweight KDU-Net -- 4 Experiments -- 4.1 Settings -- 4.2 Comparison Results -- 4.3 Robustness Evaluation -- 4.4 Hyperparameter Evaluation -- 4.5 Ablation Study -- 5 Conclusion -- References -- A Universal Non-parametric Approach for Improved Molecular Sequence Analysis -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Compression Methods -- 3.2 Problem Formulation -- 3.3 Our Algorithm -- 3.4 Distance Matrix Symmetry -- 3.5 Kernel Matrix Computation -- 3.6 Experimental Setup -- 3.7 Justification of Employing the Kernel Matrix -- 4 Results and Discussion -- 5 Conclusion -- References -- Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data -- 1 Introduction -- 2 Related Work -- 3 Methodology: NeuroGNN -- 3.1 Node Features Generation -- 3.2 Adjacency Matrix Generation -- 3.3 Prediction Using the Generated NeuroGraph -- 3.4 Pretraining -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 4.3 Ablation Study -- 4.4 Analysis of Graph Embeddings. ; 4.5 Handling Scarce Training Data -- 5 Conclusion -- References -- A Novel Population Graph Neural Network Based on Functional Connectivity for Mental Disorders Detection -- 1 Introduction -- 2 Methods -- 2.1 Brain Connectomic Graph -- 2.2 Heterogeneous Population Graph -- 3 Dataset and Experimental Evaluation -- 3.1 Dataset and Experimental Setup -- 3.2 Cross-Validation -- 3.3 Ablation Experiment and Parameter Sensitivity Analysis -- 3.4 Interpretability Analysis -- 4 Conclusion -- References -- Weighted Chaos Game Representation for Molecular Sequence Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Vision DL Models -- 4 Experimental Evaluation -- 4.1 Baselines Models: -- 5 Results and Discussion -- 6 Conclusion -- References -- Robust Influence-Based Training Methods for Noisy Brain MRI -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Influence Function on Single Validation Point -- 3.2 Influence Function on Validation Group -- 4 Influence-Based Sample Reweighing -- 4.1 Framework -- 4.2 Calculating Sample Weights -- 5 Influence-Based Sample Perturbation -- 5.1 Framework -- 5.2 Selecting Influential Samples -- 5.3 Adding Influence-Based Perturbation -- 6 Experiments -- 6.1 Evaluation Setup -- 6.2 Proposed Methods and Baselines -- 6.3 Results -- 7 Conclusion -- References -- Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations -- 1 Introduction -- 2 Problem Definition -- 3 The Co-ReaSON Approach -- 3.1 Data Preprocessing -- 3.2 Feature Extraction -- 3.3 Co-ReaSON Predictive Model -- 4 Evaluation Setup -- 5 Evaluation Results -- 6 Related Work -- 7 Conclusion -- References -- A Data-Driven Approach for Building a Cardiovascular Disease Risk Prediction System -- 1 Introduction -- 2 The Proposed AutoML System -- 3 Experiment -- 3.1 The Dataset. ; 3.2 Evaluation Metrics.
• Sommario/riassunto: The 6-volume set LNAI 14645-14650 constitutes the proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, which took place in Taipei, Taiwan, during May 7–10, 2024. The 177 papers presented in these proceedings were carefully reviewed and selected from 720 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.
• Titolo autorizzato: Advances in Knowledge Discovery and Data Mining
• ISBN: 981-9722-38-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910855370003321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 14648