K聚类加权最小二乘支持向量机在分类中的应用

Application of K-Clustering Weighted Least Squares Support Vector Machine in Classification

数据分类作为模式识别、故障诊断技术的基础,在实际应用中常常由于系统的非线性、噪声性以及样本的不平衡采集,使得常规的分类算法存在一定的局限性。将最小二乘加权支持向量机用于分类问题,利用K聚类算法分析样本间内在关系从而确定权值系数,可以很好地减小噪声影响,补偿不同类样本数目上的不平衡,减少训练时间,提高分类正确率。通过一个图像识别过程中多类别分类实例,证明了算法在分类问题中的有效性。该方法可以成为现有方法的有效补充分析工具。

Data classification is the basis of pattern recognition and fault diagnosis technologies.In practical applications,the conventional classification algorithms have some limitations due to nonlinear,induced-noise,and imbalances in the sample collection.In this paper,weighted least squares support vector machine is utilized in the problem of classification.By means of K-clustering algorithm,the intrinsic relationship between samples is analyzed and the weight coefficients are determined such that the impact of noise can be reduced and the number imbalance of different types of samples can be compensated.Moreover,the training time may be reduced and the accuracy of classification may be improved.Finally,an example of multi-class classification for image segmentation is given to illustrate the efficiency of the proposed approach.