Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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
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Light Detection and Ranging (LIDAR) has become a very important tool to many environmental applications. This work proposes to use LIDAR and image data fusion to develop high-resolution thematic maps. A novel methodology is presented which starts building a matrix of statistics from spectral and spatial information by feature extraction on the available bands (RGB from images, and intensity and height from LIDAR). Then, a contextual classification is applied to generate the final map using a support vector machine (SVM) to classify every cell and the nearest neighbor (NN) rule to sequentially reclassify each cell. The results obtained by this novel method, called SVMNNS (SVM and NN Stacking), are compared with non-contextual and contextual SVMs. It is shown that SVMNNS obtains the best results when applied to real data from the Iberian peninsula.