A novel hybrid model for image classification. (14th July 2011) - Liu, Yi-Ming; Yao, Min; Zhu, Rong
Recently, biological intelligent computing gains more and more attention in analysing large-scale real world datasets. Because the performance of the support vector machine (SVM) classifier is always degraded by poor feature subsets and inappropriate parameters for training, an improved quantum-behaved particle swarm optimisation (IQPSO) is introduced to optimise the features and parameters synchronically, aiming to improve the generalisation of the SVM classifier. That is, a novel hybrid image classification model by combing SVM and IQPSO, called as IQPSO_SVM is presented in this paper. Experimental results show that the proposed IQPSO_SVM improves the classification accuracy greatly compared to the traditional SVM with grid search, and outperforms such SVM based on genetic algorithm (GA_SVM) without accuracy loss.