Ensemble-based noise detection: noise ranking and visual performance evaluation

Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based noise ranking methodology for explicit noise and outlier identification, named Noise- Rank, which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of noise detection algorithms on data with randomly injected noise. A methodology for visual performance evaluation of noise detection algorithms in the precision-recall space, named Viper, is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new noise detection algorithms into the proposed noise detection and performance evaluation workflows.