On the Construction of Extreme Learning Machine for One Class Classifier

One Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines for outlier or novelty detection. But, traditional methods based one class classifier is very time consuming due to its iterative process for various parameters tuning. This paper presents four novel different OCC methods with their ten variants based on extreme Learning Machine (ELM). As we know, threshold decision is a crucial factor in case of OCC, so, three different threshold declining criteria have been employed so far. Our proposed classifiers mainly lie in two categories i.e. out of four proposed one class classifiers, two classifiers belong to reconstruction based and two belong to boundary based. In four proposed methods, two methods perform random feature mapping and two methods perform kernel feature mapping. These methods are tested on three benchmark datasets and exhibit better performance compared to eleven traditional one class classifiers.

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