The nature of statistical learning theory
The nature of statistical learning theory
Classification of imbalanced remote-sensing data by neural networks
Pattern Recognition Letters - special issue on pattern recognition in practice V
Cooperative-Competitive Algorithms for Evolutionary Networks Classifying Noisy Digital Images
Neural Processing Letters
The Genetic Kernel Support Vector Machine: Description and Evaluation
Artificial Intelligence Review
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
Classifier ensembles: Select real-world applications
Information Fusion
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Editorial: Hybrid learning machines
Neurocomputing
Information Sciences: an International Journal
Label dependent evolutionary feature weighting for remote sensing data
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A SVM and k-NN restricted stacking to improve land use and land cover classification
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A non-parametric approach for accurate contextual classification of LIDAR and imagery data fusion
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. In addition, artificial intelligence techniques such as machine learning or evolutive computation are often applied to improve the final LULC classification. In this paper, a hybrid artificial intelligence method based on an ensemble of multiple classifiers to improve LULC map accuracy is shown. The method works in two processing levels: first, an evolutionary algorithm (EA) for label-dependent feature weighting transforms the feature space by assigning different weights to every attribute depending on the class. Then a statistical raster from LIDAR and image data fusion is built following a pixel-oriented and feature-based strategy that uses a support vector machine (SVM) and a weighted k-NN restricted stacking. A classical SVM, the original restricted stacking (R-STACK) and the current improved method (EVOR-STACK) are compared. The results show that the evolutive approach obtains the best results in the context of the real data from a riparian area in southern Spain.