基于粒计算的支持向量数据描述分类方法

Granular Computing-Driven Support Vector Data Description Approach to Classification

分类学习效果与有限训练样本的分布情况密切相关。支持向量数据描述(Support vector data description,SVDD)作为单一边界求解模型,不能良好刻画数据实际分布特征,从而导致部分目标对象落在超球以外。为了提高其分类能力,本文提出一种基于粒计算的支持向量数据描述(Granular computing-driven SVDD,GrC-SVDD)分类方法,构造多粒度层次的属性集合以及相应的多粒度超球。首先通过邻域自信息对当前粒度层的属性集合重要度进行计算,然后选择最佳属性集合对上一粒度层未达到纯度阈值的超球再训练,直到所有超球满足条件或者属性耗尽。实验部分讨论了算法参数对分类性能的影响,并通过学习获得超参数。结果表明,与SVDD及流行的分类算法相比,本文方法具有较好的分类性能。

The effect of classification learning is closely related to the distribution of limited training samples.Support vector data description(SVDD),as a single boundary solution model,cannot well describe the actual distribution characteristics of the data,resulting in some target objects falling outside the hypersphere.To improve its classification ability,this paper proposes a granular computing-driven SVDD(GrC-SVDD)classification method to construct a multi-granularity levels attribute sets and the corresponding multi-granular hyperspheres.Firstly,the importance of the attribute within the current granularity level is calculated through the neighborhood self-information.Secondly,the best attribute set is then chosen to retrain the hyperspheres that did not achieve the purity criterion at the previous granularity level,and so on until all hyperspheres meet the conditions or the attributes are exhausted.The experimental section discusses the effect of parameters on classification performance and learns hyperparameters.The experimental results show that GrC-SVDD has better classification performance compared with SVDD and popular classification methods.