Solving Complex Machine Learning Problems with Ensemble Methods
ECML/PKDD 2013, September 27, Prague, Czech Republic
Accepted Papers
Accepted Papers
- Tomas Pevny. Anomaly Detection by Bagging
- April Shen and Andrea Danyluk. Prototype Support Vector Machines: Supervised Classification in Complex Datasets
- Jerzy Błaszczyński, Jerzy Stefanowski and Marcin Szajek. Local Neighbourhood in Generalizing Bagging for Imbalanced Data
- Hua Zhang, Evgueni Smirnov, Nikolay Nikolaev, Georgi Nalbantov and Ralf Peeters. An Ensemble Approach to Combining Expert Opinions
- Mohamed Bibimoune, Haytham Elghazel and Alex Aussem. An Empirical Comparison of Supervised Ensemble Learning Approaches
- Sandro Vega-Pons and Paolo Avesani. Clustering Ensemble on Reduced Search Spaces
- Alex Sarishvili and Gerrit Hanselmann. Software Reliability prediction via two different implementations of Bayesian model averaging
- Wenrong Zeng, Xue-Wen Chen, Hong Cheng and Jing Hua. Multi-Space Learning for Image Classification Using AdaBoost and Markov Random Fields
- Mohammed Hindawi, Haytham Elghazel and Khalid Benabdeslem. Efficient semi-supervised feature selection by an ensemble approach
- Jérôme Paul, Michel Verleysen and Pierre Dupont. Identification of Statistically Significant Features from Random Forests
- Dragi Kocev, Ivica Slavkov and Sašo Džeroski. Feature ranking for multi-label classification using predictive clustering trees
Check the program of the workshop here