Machine Learning
Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
Neighborhood systems and relational databases
CSC '88 Proceedings of the 1988 ACM sixteenth annual conference on Computer science
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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)
Consistency-based search in feature selection
Artificial Intelligence
Invariance of neighborhood relation under input space to feature space mapping
Pattern Recognition Letters
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Fast pattern selection for support vector classifiers
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Sample selection via clustering to construct support vector-like classifiers
IEEE Transactions on Neural Networks
A study on reduced support vector machines
IEEE Transactions on Neural Networks
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Support vector machine (SVM) is a class of popular learning algorithms for good generalization. However, it is time-consuming in training SVM with a large set of samples. How to improve learning efficiency is one of the most important research tasks. It is known although there are many candidate training samples in learning tasks only the samples near decision boundary have influence on classification hyperplane. Finding these samples and training SVM with them may greatly decrease time and space complexity in training. Based on the observation, we introduce neighborhood based rough set model to search boundary samples. With the model, we divide a sample space into two subsets: positive region and boundary samples. What's more, we also partition the features into several subsets: strongly relevant features, weakly relevant and indispensable features, weakly relevant and superfluous features and irrelevant features. We train SVM with the boundary samples in the relevant and indispensable feature subspaces, therefore simultaneous feature and sample selection is conducted with the proposed model. Some experiments are performed to test the proposed method. The results show that the model can select very few features and samples for training; and the classification performances are kept or improved.