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Discovery Science de Annalisa Appice

Discovery Science: Lecture Notes in Computer Science, cartea 12323

Editat de Annalisa Appice, Grigorios Tsoumakas, Yannis Manolopoulos, Stan Matwin
en Limba Engleză Paperback – 15 oct 2020
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.
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Specificații

ISBN-13: 9783030615260
ISBN-10: 303061526X
Pagini: 728
Ilustrații: XXI, 706 p. 227 illus., 147 illus. in color.
Dimensiuni: 155 x 235 x 39 mm
Greutate: 1.08 kg
Ediția:1st edition 2020
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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.