A Novel Double Ensemble Algorithm for the Classification of Multi-Class Imbalanced Hyperspectral Data

Abstract

The class imbalance problem has been reported to exist in remote sensing and hinders the classification performance of many machine learning algorithms. Several technologies, such as data sampling methods, feature selection-based methods, and ensemble-based methods, have been proposed to solve the class imbalance problem. However, these methods suffer from the loss of useful information or from artificial noise, or result in overfitting. A novel double ensemble algorithm is proposed to deal with the multi-class imbalance problem of the hyperspectral image in this paper. This method first computes the feature importance values of the hyperspectral data via an ensemble model, then produces several balanced data sets based on oversampling and builds a number of classifiers. Finally, the classification results of these diversity classifiers are combined according to a specific ensemble rule. In the experiment, different data-handling methods and classification methods including random undersampling (RUS), random oversampling (ROS), Adaboost, Bagging, and random forest are compared with the proposed double random forest method. The experimental results on three imbalanced hyperspectral data sets demonstrate the effectiveness of the proposed algorithm.

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“…It has the advantages of being simple, easy to implement, and having low computational cost, which makes it widely used in many applications. During the process of building a RF model, 24 it first employs the bagging method 25 to generate multiple different sub-sample sets from the original sample set. Then, the classification and regression tree algorithms are used to train the binary decision tree and construct the meta-classifier.…”

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