C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
A perspective view and survey of meta-learning
Artificial Intelligence Review
Cancer classification using Rotation Forest
Computers in Biology and Medicine
Statistical pattern recognition in remote sensing
Pattern Recognition
Class dependent feature scaling method using naive Bayes classifier for text datamining
Pattern Recognition Letters
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Ensemble classification based on generalized additive models
Computational Statistics & Data Analysis
Expert Systems with Applications: An International Journal
Ensemble of niching algorithms
Information Sciences: an International Journal
Dual-population based coevolutionary algorithm for designing RBFNN with feature selection
Expert Systems with Applications: An International Journal
A survey of multiple classifier systems as hybrid systems
Information Fusion
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The amounts and types of remote sensing data have increased rapidly, and the classification of these datasets has become more and more overwhelming for a single classifier in practical applications. In this paper, an ensemble algorithm based on Diversity Ensemble Creation by Oppositional Relabeling of Artificial Training Examples (DECORATEs) and Rotation Forest is proposed to solve the classification problem of remote sensing image. In this ensemble algorithm, the RBF neural networks are employed as base classifiers. Furthermore, interpolation technology for identical distribution is used to remold the input datasets. These remolded datasets will construct new classifiers besides the initial classifiers constructed by the Rotation Forest algorithm. The change of classification error is used to decide whether to add another new classifier. Therefore, the diversity among these classifiers will be enhanced and the accuracy of classification will be improved. Adaptability of the proposed algorithm is verified in experiments implemented on standard datasets and actual remote sensing dataset.