The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
SVM-KM: Speeding SVMs Learning with a priori Cluster Selection and k-Means
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Concept boundary detection for speeding up SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Neighborhood Property--Based Pattern Selection for Support Vector Machines
Neural Computation
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Fast Nearest Neighbor Condensation for Large Data Sets Classification
IEEE Transactions on Knowledge and Data Engineering
HCL2000 - A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Scaling up support vector machines using nearest neighbor condensation
IEEE Transactions on Neural Networks
Selecting Critical Patterns Based on Local Geometrical and Statistical Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary-based selection of generalized instances for imbalanced classification
Knowledge-Based Systems
Simple instance selection for bankruptcy prediction
Knowledge-Based Systems
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Sample selection via clustering to construct support vector-like classifiers
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Support vector machine (SVM) has shown prominent performance for binary classification. How to effectively apply it to massive datasets with large number of classes and instances is still a serious challenge. Instance selection methods have been proposed and shown significant efficacy for reducing the training complexity of SVM, but more or less trade off the generalization performance. This paper presents an instance selection method especially for multi-class problems. With cluster centers of positive class as reference points instances are selected for each one-versus-rest SVM model. The purpose of clustering here is to improve the efficiency of instance selection, other than to select instances directly from clusters as previous methods did. Experiments on a wide variety of datasets demonstrate that the proposed method selects fewer instances than most competitive algorithms and keeps the highest classification accuracy on most datasets. Additionally, experimental results show that this method also performs superiorly for binary problems.