Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
The Genetic Kernel Support Vector Machine: Description and Evaluation
Artificial Intelligence Review
Model-based transductive learning of the kernel matrix
Machine Learning
Combination of support vector machines using genetic programming
International Journal of Hybrid Intelligent Systems
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
A kernel path algorithm for support vector machines
Proceedings of the 24th international conference on Machine learning
Evolving Kernel Functions for SVMs by Genetic Programming
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Genetic programming for kernel-based learning with co-evolving subsets selection
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Multi-objective model selection for support vector machines
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Kernel-based methods have shown significant performances in solving supervised classification problems. However, there is no rigorous methodology capable to learn or to evolve the kernel function together with its parameters. In fact, most of the classic kernel-based classifiers use only a single kernel, whereas the real-world applications have emphasized the need to consider a combination of kernels - also known as a multiple kernel (MK) - in order to boost the classification accuracy by adapting better to the characteristics of the data. Our aim is to propose an approach capable to automatically design a complex multiple kernel (CMK) and to optimise its parameters by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. Each GP chromosome is a tree that encodes the mathematical expression of a MK function. Numerical experiments show that the SVM involving our evolved complex multiple kernel (eCMK) perform better than the classical simple kernels. Moreover, on the considered data sets, our eCMK outperform both a state of the art convex linear MK (cLMK) and an evolutionary linear MK (eLMK). These results emphasize the fact that the SVM algorithm requires a combination of kernels more complex than a linear one.