一种基于ICA的特征Bagging支持向量机集成方法

ICA-based attribute Bagging support vector machine integration method

为提高支持向量机集成的泛化性能,提出一种基于独立成分分析法的特征Bagging支持向量机集成方法,删除了冗余特征.该方法从得到的独立成分特征空间中提取特征子空间,避免了直接从原特征空间中随机选择特征子空间而导致的对特征依赖或相关性的破坏,提高了个体支持向量机的性能,保证了个体支持向量机之间的差异度.在UCI和Stat-Log数据集合上的仿真实验表明,该方法具有更好的泛化性能.

An attribute bagging support vector machine integration method based on independent component analysis （ICA） was developed to improve the generalization performance of support vector machine（SVM）. The redundant feature was deleted, and feature subspace was extracted from feature space of independent element, which avoid the destruction of attribute dependence or attribute relativity caused by selecting sub-feature space from original feature space randomly. The performance of single SVM is improved and the diversity between each other is also ensured. Simulations on UCI and StatLog datasets show that the proposed method has better generalization performance.