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