C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Classification of imbalanced remote-sensing data by neural networks
Pattern Recognition Letters - special issue on pattern recognition in practice V
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Data Mining and Knowledge Discovery
Machine Learning
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
The class imbalance problem: A systematic study
Intelligent Data Analysis
Effective Feature Space Reduction with Imbalanced Data for Semantic Concept Detection
SUTC '08 Proceedings of the 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008)
Application of distributed SVM architectures in classifying forest data cover types
Computers and Electronics in Agriculture
Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training Data
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Gene Selection for Microarray Expression Data with Imbalanced Sample Distributions
IJCBS '09 Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Evolutionary Computation
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Feature Selection with High-Dimensional Imbalanced Data
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Combating the Small Sample Class Imbalance Problem Using Feature Selection
IEEE Transactions on Knowledge and Data Engineering
Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA and a supervised filter are applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of combining several techniques to tackle the imbalance and the high dimensionality problems, and also to evaluate the order of application that leads to the best classification performance. Experimental results demonstrate the significance of using together these two preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order corresponds to first a resampling strategy and then a feature (or extraction) selection algorithm, this is a question that still needs a much more thorough investigation in the future.