Adapting Noise Filters for Ranking

Abstract

Noise filtering can be considered an important pre-processing step in the data mining process, making data more reliable for pattern extraction. An interesting aspect for increasing data understanding would be to rank the potential noisy cases, in order to evidence the most unreliable instances to be further examined. Since the majority of the filters from the literature were designed only for hard classification, distinguishing whether an example is noisy or not, in this paper we adapt the output of some state of the art noise filters for ranking the cases identified as suspicious. We also present new evaluation measures for the noise rankers designed, which take into account the ordering of the detected noisy cases.