Axiomatics for fuzzy rough sets
Fuzzy Sets and Systems
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
An axiomatic characterization of a fuzzy generalization of rough sets
Information Sciences—Informatics and Computer Science: An International Journal
A Feature Selection Newton Method for Support Vector Machine Classification
Computational Optimization and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable training of L1-regularized log-linear models
Proceedings of the 24th international conference on Machine learning
Direct convex relaxations of sparse SVM
Proceedings of the 24th international conference on Machine learning
Supervised feature selection via dependence estimation
Proceedings of the 24th international conference on Machine learning
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Boosting sex identification performance
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
FRSVMs: Fuzzy rough set based support vector machines
Fuzzy Sets and Systems
Quadratic Programming Feature Selection
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Recent advances on machine learning and Cybernetics
SVM classifier based feature selection using GA, ACO and PSO for siRNA design
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Multiobjective simulated annealing based approach for feature selection in anaphora resolution
DAARC'11 Proceedings of the 8th international conference on Anaphora Processing and Applications
On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems
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Generalized Multiple Kernel Learning (GMKL) has been proposed in the literature for feature selection. GMKL learns linear, product and exponential combinations of given base kernels which makes it more robust and efficient than traditional Multiple Kernel Learning (MKL). GMKL has been shown to be a good tool for feature selection as well. Time taken for the convergence of GMKL depends upon the initialization of kernel weights. Optimization schemes in GMKL initialize kernel weights randomly. This produces variability in convergence time. In this paper, we propose a Fuzzy Rough Set based kernel weight initialization for GMKL (FR-GMKL). We show that this results in faster convergence than that obtained by random initialization in GMKL while retaining same level of accuracy. We also show that the computation time of our proposed method is lower than that obtained through Quadratic Programming Feature Selection (QPFS) based as well as Maximal Relevance (MaxRel) based initialization of the regularization parameter. The performance of our proposed method is evaluated using five benchmark binary classification datasets and three benchmark multi-class classification datasets from the UCI repository.