Discovery Science : 23rd International Conference, DS 2020, Thessaloniki, Greece, October 19-21, 2020, Proceedings / edited by Annalisa Appice, Grigorios Tsoumakas, Yannis Manolopoulos, Stan Matwin.

Format:

Book

Contributor:

Appice, Annalisa, Editor.

Tsoumakas, Grigorios., Editor.

Manolopoulos, Yannis., Editor.

Matwin, Stan, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 12323

Lecture Notes in Artificial Intelligence, 2945-9141 ; 12323

Language:

English

Subjects (All):

Artificial intelligence.

Application software.

Education-Data processing.

Data mining.

Information storage and retrieval systems.

Artificial Intelligence.

Computer and Information Systems Applications.

Computers and Education.

Data Mining and Knowledge Discovery.

Information Storage and Retrieval.

Local Subjects:

Artificial Intelligence.

Computer and Information Systems Applications.

Computers and Education.

Data Mining and Knowledge Discovery.

Information Storage and Retrieval.

Physical Description:

1 online resource (XXI, 706 pages) : 227 illustrations, 147 illustrations in color.

Edition:

1st ed. 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

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.

Contents:

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

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.

Other Format:

Printed edition:

ISBN:

978-3-030-61527-7

9783030615277

Access Restriction:

Restricted for use by site license.