L
Li Guo
Researcher at University of Bordeaux
Publications - 14
Citations - 897
Li Guo is an academic researcher from University of Bordeaux. The author has contributed to research in topics: Ensemble learning & Random forest. The author has an hindex of 8, co-authored 13 publications receiving 779 citations. Previous affiliations of Li Guo include University of Lyon & Centre national de la recherche scientifique.
Papers
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Journal ArticleDOI
Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests
TL;DR: The Random Forests algorithm is chosen as a classifier: it runs efficiently on large datasets, and provides measures of feature importance for each class, and the relevance of full-waveform lidar features is demonstrated for building and vegetation area discrimination.
Airborne lidar feature selection for urban classification using random forests
TL;DR: Multiple classifers are applied to lidar feature selection for urban scene classification using Random forests since they provide an accurate classification and run efficiently on large datasets.
Journal ArticleDOI
Margin-based ordered aggregation for ensemble pruning
Li Guo,Samia Boukir +1 more
TL;DR: This paper presents a new ensemble pruning method which highly reduces the complexity of ensemble methods and performs better than complete bagging in terms of classification accuracy and is a very fast algorithm.
Journal ArticleDOI
Fast data selection for SVM training using ensemble margin
Li Guo,Samia Boukir +1 more
TL;DR: A new ensemble margin-based data selection approach that relies on a simple and efficient heuristic to provide support vector candidates: selecting lowest margin instances and significantly reduces the SVM training task complexity while maintaining the accuracy of the S VM classification.
Proceedings ArticleDOI
Support Vectors Selection for Supervised Learning Using an Ensemble Approach
TL;DR: This work presents a new efficient support vector selection method based on ensemble margin, a key concept in ensemble classifiers that exploits a new version of the margin of an ensemble-based classification and selects the smallest margin instances as support vectors.