Discovery Science : 24th International Conference, DS 2021, Halifax, NS, Canada, October 11-13, 2021, Proceedings / edited by Carlos Soares, Luis Torgo.

Format:

Book

Contributor:

Soares, Carlos, Editor.

Torgo, Luís, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 12986

Lecture Notes in Artificial Intelligence ; 12986

Language:

English

Subjects (All):

Artificial intelligence.

Social sciences-Data processing.

Data mining.

Computer networks.

Education-Data processing.

Artificial Intelligence.

Computer Application in Social and Behavioral Sciences.

Data Mining and Knowledge Discovery.

Computer Communication Networks.

Computers and Education.

Local Subjects:

Artificial Intelligence.

Computer Application in Social and Behavioral Sciences.

Data Mining and Knowledge Discovery.

Computer Communication Networks.

Computers and Education.

Physical Description:

1 online resource (XII, 474 pages) : 26 illustrations

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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. .

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-88942-5

9783030889425

Access Restriction:

Restricted for use by site license.