Abstract: 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 estimators with the introduced parameter,MCDOCC is more sensitive in the low-density region located on boundary,alleviates 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 MCDOCC with Negative data (NMCDOCC) 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.

Key words: One-class classifier,Probability density estimation,Maximum constrained density,Prior knowledge