Journal Article10.1504/IJCSE.2016.080212
Differential evolution-based parameters optimisation and feature selection for support vector machine
Jun Li,Lixin Ding,Bo Li +2 more
- 01 Jan 2016
- Vol. 13, Iss: 4, pp 355-363
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TL;DR: The obtained results suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed DE-SVM-FS classification system.
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Abstract: This paper addresses the problem of SVM classification optimisation. For this purpose, the authors propose an SVM classification system based on differential evolution DE to improve the generalisation performance of the SVM classifier. In the classification system, a method of simultaneous parameters optimisation and feature selection for support vector machine is put forward. The experiments are conducted on the basis of benchmark dataset. The obtained results clearly confirm the superiority of the DE-SVM-FS approach compared to default SVM classifier and DE-SVM algorithm; this suggests that further substantial improvements in terms of classification accuracy can be achieved by the proposed DE-SVM-FS classification system.
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Citations
Construction of Near-Optimal Axis-Parallel Decision Trees Using a Differential-Evolution-Based Approach
TL;DR: A differential-evolution-based approach implementing a global search strategy to find a near-optimal axis-parallel decision tree is introduced and a statistical analysis suggests that this approach is better as a decision tree induction method as compared with other supervised learning methods.
A Multi-class SVM Based Content Based Image Retrieval System Using Hybrid Optimization Techniques
TL;DR: Content-Based Image Retrieval is proposed in this work, taking features based on Exact Legendre Moments, HVS color quantization with dc coefficient and statistical properties such as variance, mean, and skew of Conjugate Symmetric Sequency Complex Hadamard Transform (CS-SCHT).
OC1-DE: A Differential Evolution Based Approach for Inducing Oblique Decision Trees
Rafael Rivera-López,Juana Canul-Reich,José A. Gámez,José M. Puerta +3 more
- 11 Jun 2017
TL;DR: This paper describes the application of a Differential Evolution based approach for inducing oblique decision trees in a recursive partitioning strategy and results show that this approach induces more accurate classifiers than those produced by other proposed induction methods.
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A Robust Adaptive Hierarchical Learning Crow Search Algorithm for Feature Selection
TL;DR: Wang et al. as discussed by the authors proposed an adaptive hierarchical learning crow search algorithm (AHL-CSA) to solve the problem of feature selection, which can eliminate irrelevant and redundant features and improve the accuracy of classification at the same time.
11
Differential Evolution Algorithm in the Construction of Interpretable Classification Models
Rafael Rivera-López,Juana Canul-Reich +1 more
- 27 Jun 2018
TL;DR: Two methods are described in this chapter: one implementing a recursive partitioning strategy to find the most suitable oblique hyperplane of each internal node of a decision tree, and the other conducting a global search of a near-optimal oblique decision tree.
References
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
•Book
The Nature of Statistical Learning Theory
Vladimir Vapnik
- 01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
46K
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Regularization Networks and Support Vector Machines
TL;DR: Both formulations of regularization and Support Vector Machines are reviewed in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.