Improving classification accuracy of AVIRIS data by means of classifier combination

摘要

In this paper we study the use of classifier combination for improving the classification accuracy of A IRIS data. Two types of combination ensembles are used as high-level classifiers, cascading and voting. Regarding the base-level classifiers, we use limited depth decision trees and the nearest neighbor classifier (k-NN). The final classification system uses a threshold parameter that allows the user to specify a trade-off between classification accuracy and the percentage of classified samples. Dimensionality reduction is carried out by using decision trees in order to select the most promising classification features, which will be used to build the base-level classifiers. We also use classical statistical analysis to measure correlation between spectral bands. A set of post-processing rules may be also applied to generate large homogeneous regions from the pixmap generated by the classifier: false spots and "unknown" samples may be re-classified depending on their neighborhood. Experiments show that the combined use of cascading small decision trees and a voting scheme with a k-NN classifier, improves classification performance, when compared to a single classifier, while the the "unknown" class allows us to identify the possible outliers present in the training set. The use of post-processing generates large regions which may be more useful for classification and interpretation.