Discovery Science

23rd International Conference, DS 2020, Thessaloniki, Greece, October 19–21, 2020, Proceedings

Häftad, Engelska, 2020

Av Annalisa Appice, Grigorios Tsoumakas, Yannis Manolopoulos, Stan Matwin

1 239 kr

Beställningsvara. Skickas inom 10-15 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

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.

Produktinformation

Utgivningsdatum2020-10-15
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieLecture Notes in Artificial Intelligence
Antal sidor706
FörlagSpringer Nature Switzerland AG
ISBN9783030615260

Tillhör följande kategorier

Tillämpad datateknik inom Data och it
Systemvetenskap och AI inom Data och it
Material och digital teknik i undervisningen inom Psykologi och pedagogik
Artificiell intelligens inom Data och it
Databaser inom Data och it

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.