Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace
- 1 October 2011
- journal article
- Published by Springer Science and Business Media LLC in Journal of Iron and Steel Research International
- Vol. 18 (10), 17-23
- https://doi.org/10.1016/s1006-706x(12)60016-8
Abstract
No abstract availableKeywords
This publication has 14 references indexed in Scilit:
- Cost-conscious multiple kernel learningPattern Recognition Letters, 2010
- A Novel Fault Diagnosis System for Blast Furnace Based on Support Vector Machine EnsembleISIJ International, 2010
- A novel LS-SVMs hyper-parameter selection based on particle swarm optimizationNeurocomputing, 2008
- Evaluation of Scheme Design of Blast Furnace Based on Artificial Neural NetworkJournal of Iron and Steel Research International, 2008
- A novel approach to estimation of E. coli promoter gene sequences: Combining feature selection and least square support vector machine (FS_LSSVM)Applied Mathematics and Computation, 2007
- Text classification: A least square support vector machine approachApplied Soft Computing, 2007
- Fault diagnostics based on particle swarm optimisation and support vector machinesMechanical Systems and Signal Processing, 2007
- Pattern Detection of Atherosclerosis from Carotid Artery Doppler Signals using Fuzzy Weighted Pre-Processing and Least Square Support Vector Machine (LSSVM)Annals of Biomedical Engineering, 2007
- Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonistsEuropean Journal of Pharmaceutical Sciences, 2004
- A comparison of methods for multiclass support vector machinesIEEE Transactions on Neural Networks, 2002