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Discovery Science de Carlos Soares

Discovery Science: Lecture Notes in Computer Science, cartea 12986

Editat de Carlos Soares, Luis Torgo
en Limba Engleză Paperback – 9 oct 2021
This book constitutes the proceedings of the 24th International Conference on Discovery Science, DS 2021, which took place virtually during October 11-13, 2021. The 36 papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: applications; classification; data streams; graph and network mining; machine learning for COVID-19; neural networks and deep learning; preferences and recommender systems; representation learning and feature selection; responsible artificial intelligence; and spatial, temporal and spatiotemporal data.
 
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Specificații

ISBN-13: 9783030889418
ISBN-10: 3030889416
Pagini: 488
Ilustrații: XII, 474 p. 26 illus.
Dimensiuni: 155 x 235 x 27 mm
Greutate: 0.73 kg
Ediția:1st edition 2021
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Applications.- Automated Grading of Exam Responses: An Extensive Classification Benchmark.- Automatic human-like detection of code smells.- HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM.- Predicting reach to find persuadable customers: improving uplift models for churn prevention.- Classification.- A Semi-Supervised Framework for Misinformation Detection.- An Analysis of Performance Metrics for Imbalanced Classification.- Combining Predictions under Uncertainty: The Case of Random Decision Trees.- Shapley-Value Data Valuation for Semi-Supervised Learning.- Data streams.- A Network Intrusion Detection System for Concept Drifting Network Traffic Data.- Incremental k-Nearest Neighbors Using Reservoir Sampling for Data Streams.- Statistical Analysis of Pairwise Connectivity.- Graph and Network Mining.- FHA: Fast Heuristic Attack against Graph Convolutional Networks.- Ranking Structured Objects with Graph Neural Networks.- Machine Learning for COVID-19.- Knowledge discovery of the delays experienced in reporting covid19 confirmed positive cases using time to event models.- Multi-Scale Sentiment Analysis of Location-Enriched COVID-19 Arabic Social Data.- Prioritization of COVID-19 literature via unsupervised keyphrase extraction and document representation learning.- Sentiment Nowcasting during the COVID-19 Pandemic.- Neural Networks and Deep Learning.- A Sentence-level Hierarchical BERT Model for Document Classification with Limited Labelled Data.- Calibrated Resampling for Imbalance and Long-Tails in Deep learning.- Consensus Based Vertically Partitioned Multi-Layer Perceptrons for Edge Computing.- Controlling BigGAN Image Generation with a Segmentation Network.- GANs for tabular healthcare data generation: a review on utility and privacy.- Preferences and Recommender Systems.- An Ensemble Hypergraph Learning framework for Recommendation.- KATRec: Knowledge Aware aTtentive Sequential Recommendations.- Representation Learning and Feature Selection.- Elliptical Ordinal Embedding.- Unsupervised Feature Ranking via Attribute Networks.- Responsible Artificial Intelligence.- Deriving a Single Interpretable Model by Merging Tree-based Classifiers.- Ensemble of Counterfactual Explainers. Riccardo Guidotti and Salvatore Ruggieri.- Learning Time Series Counterfactuals via Latent Space Representations.- Leveraging Grad-CAM to Improve the Accuracy of Network Intrusion Detection Systems.- Local Interpretable Classifier Explanations with Self-generated Semantic Features.- Privacy risk assessment of individual psychometric profiles.- The Case for Latent Variable vs Deep Learning Methods in Misinformation Detection: An Application to COVID-19.- Spatial, Temporal and Spatiotemporal Data.- Local Exceptionality Detection in Time Series Using Subgroup Discovery.- Neural Additive Vector Autoregression Models for Causal Discovery in Time Series.- Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data.