A Review of Novelty Detection and Experimental Analysis Based on Benchmark Datasets
Chen Beibei · Journal of Putian University · 2012
In order to know more about the state-of-the-art methods and to konw the differences between them,this paper firstly presents a brief taxonomy review of the existing literature on novelty detection,and then focuses on experimental analysis and performance(False Negative Rate,False Positive Rate and Area Under the Receiver Operating Characteristics Curve) comparison based on ten different benchmark datasets.Finally,it concludes that Gaussian Mixture method outperforms the other three ones,which all the five methods are selected as representative algorithms from different categories of novelty detection.