Credit reports : consumers' ability to dispute and change inaccurate information : hearing before the Committee on Financial Services, U.S. House of Representatives, One Hundred Tenth Congress, first session, June 19, 2007

• Record Nr.: UNINA9910694325803321
• Soggetti: Credit ratings - United States; Consumer credit - United States; Consumer protection - United States; Disclosure of information - United States
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

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

• Record Nr.: UNINA9910502614603321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
• ISBN: 3-030-88942-4
• Edizione: [1st ed. 2021.]
• Descrizione fisica: 1 online resource (474 pages)
• Collana: Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 12986
• Disciplina: 006.3
• Soggetti: 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
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: 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.
• Sommario/riassunto: 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. .