Appice / Tsoumakas / Manolopoulos Discovery Science

23rd International Conference, DS 2020, Thessaloniki, Greece, October 19–21, 2020, Proceedings
1. Auflage 2020
ISBN: 978-3-030-61527-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 12323, 706 Seiten, eBook

Reihe: Lecture Notes in Computer Science

This book constitutes the proceedings of the 23rd International Conference on Discovery Science, DS 2020, which took place during October 19-21, 2020. The conference was planned to take place in Thessaloniki, Greece, but had to change to an online format due to the COVID-19 pandemic. The 26 full and 19 short papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: classification; clustering; data and knowledge representation; data streams; distributed processing; ensembles; explainable and interpretable machine learning; graph and network mining; multi-target models; neural networks and deep learning; and spatial, temporal and spatiotemporal data.

Zielgruppe

Research

Autoren/Hrsg.

Appice, Annalisa

Tsoumakas, Grigorios

Manolopoulos, Yannis

Matwin, Stan

Inhaltsverzeichnis

Classification.-
Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach.- Mitigating Discrimination in Clinical Machine Learning Decision Support using Algorithmic Processing Techniques.- WeakAL: Combining Active Learning and Weak Supervision.-
Clustering.-
Constrained Clustering via Post-Processing.- Deep Convolutional Embedding for Painting Clustering: Case Study on Picasso's Artworks.- Dynamic Incremental Semi-Supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction.- Iterative Multi-Mode Discretization: Applications to Co-Clustering.-
Data and Knowledge Representation.-
COVID-19 Therapy Target Discovery with Context-aware Literature Mining.- Semantic Annotation of Predictive Modelling Experiments.- Semantic Description of Data Mining Datasets: An Ontology-based Annotation Schema.-
Data Streams
.- FABBOO - Online Fairness-aware Learning under Class Imbalance.- FEAT: A Fairness-enhancing andConcept-adapting Decision Tree Classifer.- Unsupervised Concept Drift Detection using a Student{Teacher Approach.-
Dimensionality Reduction and Feature Selection.-
Assembled Feature Selection For Credit Scoring in Micro nance With Non-Traditional Features.- Learning Surrogates of a Radiative Transfer Model for the Sentinel 5P Satellite.- Nets versus Trees for Feature Ranking and Gene Network Inference.- Pathway Activity Score Learning Algorithm for Dimensionality Reduction of Gene Expression Data.- Machine learning for Modelling and Understanding in Earth Sciences.-
Distributed Processing.-
Balancing between Scalability and Accuracy in Time-Series Classification for Stream and Batch Settings.- DeCStor: A Framework for Privately and Securely Sharing Files Using a Public Blockchain.- Investigating Parallelization of MAML.-
Ensembles
.- Extreme Algorithm Selection with Dyadic Feature Representation.- Federated Ensemble Regression using Classification.- One-Class Ensembles for Rare Genomic Sequences Identification.-
Explainable and Interpretable Machine Learning.-
Explaining Sentiment Classi cation with Synthetic Exemplars and Counter-Exemplars.- Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology.- Interpretable Machine Learning with Bitonic Generalized Additive Models and Automatic Feature Construction.- Predicting and Explaining Privacy Risk Exposure in Mobility Data.-
Graph and Network Mining.-
Maximizing Network Coverage Under the Presence of Time Constraint by Injecting Most Effective k-Links.- On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: a Link Prediction Perspective.- Simultaneous Process Drift Detection and Characterization with Pattern-based Change Detectors.-
Multi-Target Models
.- Extreme Gradient Boosted Multi-label Trees for Dynamic ClassifierChains.- Hierarchy Decomposition Pipeline: A Toolbox for Comparison of Model Induction Algorithms on Hierarchical Multi-label Classification Problems.- Missing Value Imputation with MERCS: a Faster Alternative to MissForest.- Multi-Directional Rule Set Learning.- On Aggregation in Ensembles of Multilabel Classifiers.-
Neural Networks and Deep Learning.-
Attention in Recurrent Neural Networks for Energy Disaggregation.- Enhanced Food Safety Through Deep Learning for Food Recalls Prediction.- Machine learning for Modelling and Understanding in Earth Sciences.- FairNN - Conjoint Learning of Fair Representations for Fair Decisions.- Improving Deep Unsupervised Anomaly Detection by Exploiting VAE Latent Space Distribution.-
Spatial, Temporal and Spatiotemporal Data
.- Detecting Temporal Anomalies in Business Processes using Distance-based Methods.- Mining Constrained Regions of Interest: An Optimization Approach.- Mining Disjoint Sequential Pattern Pairs from Tourist Trajectory Data.- Predicting the Health Condition of mHealth App Users with Large Differences in the Amount of Recorded Observations - Where to Learn from.- Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method.- Time Series Regression in Professional Road Cycling.