Lp-Norm Constrained One-Class Classifier Combination

Different realisations of this generic methodology may appear in accordance with the level where the fusion is practised, including data fusion, feature fusion, soft decision fusion, or hard decision fusion, etc. Classifier fusion, and in particular, a soft combination of the output scores of multiple learners has been established as a standard approach to improve classification performance in various learning scenarios [1]. The motivating principle behind adopting a classifier fusion approach is to leverage the collective ability of multiple models, presumed to be as independent as possible, to mitigate the shortcomings of a single model, thus improving the overall performance. In general, classifier fusion approaches are expected to yield better results by - reducing the risk of selecting an inaccurate individual learner; - minimising the chances of settling for a suboptimal solution when individual learners may be stuck in local optima; - allowing for a better exploration of the potential solution space; - potentially providing a better capacity to deal with imbalanced training data; - being more capable of adapting to dynamic scenarios where the representations and labels may change over time, and - helping to mitigate the curse of dimensionality and reducing the chances of overfitting [2]. Despite its appealing properties and its widespread application in multiclass classification scenarios where significant performance improvements have been observed [1], the one-class classifier fusion paradigm has not been explored widely. In a one-class classification (OCC) setting, one is interested in classifying an observation as normal/positive/target or as abnormal/negative/anomaly by mainly training on positive samples [3]. The prevalent application of OCC is often witnessed in scenarios where the accumulation of counterexamples is either highly demanding or simply infeasible [4], challenging binary/multi-class classification approaches.