Classification by fuzzy integral: performance and tests
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Choquet fuzzy integral-based hierarchical networks for decision analysis
IEEE Transactions on Fuzzy Systems
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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This paper presents a comparative evaluation between two classification strategies for the analysis of remote sensed data. The first is based on the combination of the outputs of a neural network (NN) ensemble, the second concerns the application of Support Vector Machine (SVM) classifiers. Emphasis is given to the understanding of the limits and the advantages of the two strategies to design a classifier system able to provide high generalization capability. Two sets of experiments have been carried out to classify benchmark remote sensed data. In the first set a Fuzzy Integral has been used to combine the outputs of neural classifiers in an ensemble. In the second set of experiments SVM classifiers have been trained and tested on the same data set. The comparative analysis evidences that SVM classifiers outperform an ensemble of classifiers, whose partial results are combined by a Fuzzy Integral. The training complexity of SVMs seems, however, to be a limitation to the extensive use of SVMs in complex multisource-multitemporal data classification.