Advances in Intelligent Data Analysis XVII

17th International Symposium, IDA 2018, 's-Hertogenbosch, The Netherlands, October 24-26, 2018, Proceedings
1. Auflage 2018
ISBN: 978-3-030-01767-5
Verlag: Springer International Publishing

Buch, Englisch, Band 11191, 394 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 616 g

Reihe: Lecture Notes in Computer Science

This book constitutes the conference proceedings of the 17th International Symposium on Intelligent Data Analysis, which was held in October 2018 in ‘s-Hertogenbosch, the Netherlands. The traditional focus of the IDA symposium series is on end-to-end intelligent support for data analysis. The 29 full papers presented in this book were carefully reviewed and selected from 65 submissions. The papers cover all aspects of intelligent data analysis, including papers on intelligent support for modeling and analyzing data from complex, dynamical systems.

Zielgruppe

Research

Autoren/Hrsg.

Duivesteijn, Wouter

Ukkonen, Antti

Siebes, Arno

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

Elements of an Automatic Data Scientist.- The Need for Interpretability Biases Open Data Science.- Automatic POI Matching Using an Outlier Detection Based Approach.- Fact Checking from Natural Text with Probabilistic Soft Logic.- ConvoMap: Using Convolution to Order Boolean Data.- Training Neural Networks to distinguish craving smokers, non-craving smokers, and non-smokers.- Missing Data Imputation via Denoising Autoencoders: the untold story.- Online Non-Linear Gradient Boosting in Multi-Latent Spaces.- MDP-based Itinerary Recommendation using Geo-Tagged Social Media.- Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization.- Non-Negative Local Sparse Coding for Subspace Clustering.- Pushing the Envelope in Overlapping Communities Detection.-Right for the Right Reason: Training Agnostic Networks.- Link Prediction in Multi-Layer Networks and its Application to Drug Design.- A hierarchical Ornstein-Uhlenbeck model for stochastic time series analysis.- Analysing the footprint of classi_ers in overlapped and imbalanced contexts.- Tree-based Cost Sensitive Methods for Fraud Detection in Imbalanced Data.- Reduction Stumps for Multi-Class Classification.- Decomposition of quantitative Gaifman graphs as a data analysis tool.- Exploring the Effects of Data Distribution in Missing Data Imputation.- Communication-free Widened Learning of Bayesian Network Classifiers Using Hashed Fiedler Vectors.- Expert finding in Citizen Science platform for biodiversity monitoring via weighted PageRank algorithm.- Random forests with latent variables to foster feature selection in the context of highly correlated variables. Illustration with a bioinformatics application.-Don't Rule Out Simple Models Prematurely: a Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML.- Detecting Shifts in Public Opinion: a big data study of global news content.- Biased Embeddings from Wild Data: Measuring, Understanding and Removing.- Real-Time Excavation Detection at Construction Sites using Deep Learning.- COBRAS: Interactive Clustering with Pairwise Queries.- Automatically Wrangling Spreadsheets into Machine Learning Data Formats.- Learned Feature Generation for Molecules.