最大化约束密度单类分类器

Maximum Constrained Density One-class Classifier

针对单类分类器设计中的密度方法,采用以任务为导向的设计思想,通过人为指定核密度估计的密度函数上界,增强了边界低密度区域数据敏感性,同时也有效降低了密度估计的计算复杂度。进一步最大化全体样本的核密度估计函数并采用线性规划,可快速得到相应的稀疏解,因而称之为最大化约束密度单类分类器(Maximum constrained density based one-class classifier,MCDOCC)。为充分利用单类数据中可能出现的极少量异常数据,进一步提出了带负类的最大化约束密度分类器(MCDOCC with negative data,NMCDOCC),通过挖掘异常数据的先验信息来修正仅有正常类的数据描述边界,可提高分类器泛化能力。UCI数据集上的实验结果表明,MCDOCC的泛化能力与单类支持向量机相当,NMCDOCC较之则有所提高,从而能够更高效地估计目标类数据概率密度。

A novel One-Class Classifier （OCC） was proposed within the framework of probability density estimation called Maximum constrained density based OCC, MCDOCC. By constraining the upper bound of the kernel density esti- mators with the introduced parameter, MCIX）CC is more sensitive in the low-density region located on boundary, allevi- ates the computation cost at the same time. Then, through maximizing the average constrained density of the target data, MCDOCC optimizes the object function with linear programming and the sparse solution can be reached finally. To further improve the generalization ability, two ways for MCIX）CC with Negative data （NMCI）OCC） were developed for full utilizing the prior knowledge existed in outliers. Experimental results on UCI data sets show that the generalization ability of MCDOCC is comparable with one-class support vector machines, but NMCDOCC is better than it.