The nature of statistical learning theory
The nature of statistical learning theory
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Boosting prediction accuracy on imbalanced datasets with SVM ensembles
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Influence of Hyperparameters on Random Forest Accuracy
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A Rotary-wing Unmanned Air Vehicle for Aquatic Weed Surveillance and Management
Journal of Intelligent and Robotic Systems
Eggshell crack detection using a wavelet-based support vector machine
Computers and Electronics in Agriculture
Review: Development of soft computing and applications in agricultural and biological engineering
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
A comparative study of the multi-objective optimization algorithms for coal-fired boilers
Expert Systems with Applications: An International Journal
Classification of high dimensional and imbalanced hyperspectral imagery data
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Inconsistency-based active learning for support vector machines
Pattern Recognition
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In many 'real-world' applications, a classification of large data sets, which are often also imbalanced, is difficult due to the small, but usually more interesting classes. In this study, a large data set, forest cover type classes, which is actually multi-class classification defined with seven imbalanced classes and used as a resource inventory information was analyzed and evaluated. The data set was transformed into seven new data sets and a support vector machine (SVM) was employed to solve a binary classification problem of balanced and imbalanced data sets with various sizes. In the two approaches considered, the use of distributed SVM architectures, which basically reduces the complexity of the quadratic optimization problem of very large data sets, and the use of two sampling approaches for classification of imbalanced data sets were combined and results presented. The experimental results of distributed SVM architectures show the improvement of the accuracy for larger data sets in comparison to a single SVM classifier and their ability to improve the correct classification of the minority class.