Variable precision rough set model
Journal of Computer and System Sciences
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
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
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IEEE Intelligent Systems
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
Combined SVM-Based Feature Selection and Classification
Machine Learning
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Subset based least squares subspace regression in RKHS
Neurocomputing
Support vector machine for functional data classification
Neurocomputing
Fast pattern selection for support vector classifiers
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Neighborhood systems and approximate retrieval
Information Sciences: an International Journal
Sample selection via clustering to construct support vector-like classifiers
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A study on reduced support vector machines
IEEE Transactions on Neural Networks
Binary tree of SVM: a new fast multiclass training and classification algorithm
IEEE Transactions on Neural Networks
Feature subset selection Filter-Wrapper based on low quality data
Expert Systems with Applications: An International Journal
Mixed feature selection in incomplete decision table
Knowledge-Based Systems
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Support vector machines (SVMs) are a class of popular classification algorithms for their high generalization ability. However, it is time-consuming to train SVMs with a large set of learning samples. Improving learning efficiency is one of most important research tasks on SVMs. It is known that although there are many candidate training samples in some learning tasks, only the samples near decision boundary which are called support vectors have impact on the optimal classification hyper-planes. Finding these samples and training SVMs with them will greatly decrease training time and space complexity. Based on the observation, we introduce neighborhood based rough set model to search boundary samples. Using the model, we firstly divide sample spaces into three subsets: positive region, boundary and noise. Furthermore, we partition the input features into four subsets: strongly relevant features, weakly relevant and indispensable features, weakly relevant and superfluous features, and irrelevant features. Then we train SVMs only with the boundary samples in the relevant and indispensable feature subspaces, thus feature and sample selection is simultaneously conducted with the proposed model. A set of experimental results show the model can select very few features and samples for training; in the mean time the classification performances are preserved or even improved.