[edit]

Volume 74: First International Workshop on Learning with Imbalanced Domains: Theory and Applications, 22 September 2017, ECML-PKDD, Skopje, Macedonia

[edit]

Editors: Luís Torgo, Bartosz Krawczyk, Paula Branco, Nuno Moniz

[bib][citeproc]

Contents:

Preface

Learning with Imbalanced Domains: Preface

Luís Torgo, Bartosz Krawczyk, Paula Branco, Nuno Moniz; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:1-6

Full Papers

Influence of minority class instance types on SMOTE imbalanced data oversampling

Przemysław Skryjomski, Bartosz Krawczyk; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:7-21

A Network Perspective on Stratification of Multi-Label Data

Piotr Szymański, Tomasz Kajdanowicz; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:22-35

SMOGN: a Pre-processing Approach for Imbalanced Regression

Paula Branco, Luís Torgo, Rita P. Ribeiro; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:36-50

Stacked-MLkNN: A stacking based improvement to Multi-Label k-Nearest Neighbours

Arjun Pakrashi, Brian Mac Namee; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:51-63

Sampling a Longer Life: Binary versus One-class classification Revisited

Colin Bellinger, Shiven Sharma, Osmar R. Zaı̈ane, Nathalie Japkowicz; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:64-78

Improving Resampling-based Ensemble in Churn Prediction

Bing Zhu, Seppe Broucke, Bart Baesens, Sebastián Maldonado; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:79-91

Predicting Defective Engines using Convolutional Neural Networks on Temporal Vibration Signals

Nikou Günnemann, Jürgen Pfeffer; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:92-102

Effect of Data Imbalance on Unsupervised Domain Adaptation of Part-of-Speech Tagging and Pivot Selection Strategies

Xia Cui, Frans Coenen, Danushka Bollegala; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:103-115

Posters

Tunable Plug-In Rules with Reduced Posterior Certainty Loss in Imbalanced Datasets

Emmanouil Krasanakis, Eleftherios Spyromitros-Xioufis, Symeon Papadopoulos, Yiannis Kompatsiaris; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:116-128

Evaluation of Ensemble Methods in Imbalanced Regression Tasks

Nuno Moniz, Paula Branco, Luís Torgo; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:129-140

Controlling Imbalanced Error in Deep Learning with the Log Bilinear Loss

Yehezkel S. Resheff, Amit Mandelbom, Daphna Weinshall; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:141-151

Unsupervised Classification of Speaker Profiles as a Point Anomaly Detection Task

Cedric Fayet, Arnaud Delhay, Damien Lolive, Pierre-François Marteau; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:152-163

Dealing with the task of imbalanced, multidimensional data classification using ensembles of exposers

Paweł Ksieniewicz, Michał Woźniak; Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:164-175

subscribe via RSS