The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
New reflection generator for simulated annealing in mixed-integer/continuous global optimization
Journal of Optimization Theory and Applications
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
A neural network model with bounded-weights for pattern classification
Computers and Operations Research
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Combined SVM-Based Feature Selection and Classification
Machine Learning
Neural Networks - 2005 Special issue: IJCNN 2005
Software reliability forecasting by support vector machines with simulated annealing algorithms
Journal of Systems and Software
Feature extraction and gating techniques for ultrasonic shaft signal classification
Applied Soft Computing
Text classification: A least square support vector machine approach
Applied Soft Computing
Ex-ray: Data mining and mental health
Applied Soft Computing
Support vector machines based on K-means clustering for real-time business intelligence systems
International Journal of Business Intelligence and Data Mining
Improved feature selection algorithm based on SVM and correlation
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Artificial Intelligence in Medicine
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fast and efficient strategies for model selection of Gaussian support vector machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Knowledge and Information Systems
An improved method of traffic forecasting based on tariff-SASVR
ICNC'09 Proceedings of the 5th international conference on Natural computation
Tuning SVM parameters by using a hybrid CLPSO-BFGS algorithm
Neurocomputing
Enhancing the classification accuracy by scatter-search-based ensemble approach
Applied Soft Computing
Applying electromagnetism-like mechanism for feature selection
Information Sciences: an International Journal
Face prediction from fMRI data during movie stimulus: strategies for feature selection
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Feature evaluation and selection with cooperative game theory
Pattern Recognition
A novel algorithm applied to classify unbalanced data
Applied Soft Computing
Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems
International Journal of Applied Metaheuristic Computing
Feature selection for medical diagnosis: Evaluation for cardiovascular diseases
Expert Systems with Applications: An International Journal
A threshold fuzzy entropy based feature selection for medical database classification
Computers in Biology and Medicine
Least squares twin parametric-margin support vector machine for classification
Applied Intelligence
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
Evolutionary approach for automated component-based decision tree algorithm design
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
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Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM. To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM.