Record Nr.

UNISA996418318903316

Titolo

Discovery science : 23rd International Conference, DS 2020, Thessaloniki, Greece, October 19-21, 2020, proceedings / / Annalisa Appice [and three others] editors

Pubbl/distr/stampa


Cham, Switzerland : , : Springer, , [2020]
Â©2020

ISBN

3-030-61527-8

Edizione

[1st ed. 2020.]

Descrizione fisica

1 online resource (XXI, 706 p. 227 illus., 147 illus. in color.)

Collana

Lecture Notes in Artificial Intelligence ; ; 12323

Disciplina

501

Soggetti

Science - Philosophy

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

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 and Concept-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 Classifier Chains -- 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.

Sommario/riassunto

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.

Record Nr.

UNIORUON00308779

Autore

HAZLITT, William