Dealing with Noisy Data

This chapter focuses on the noise imperfections of the data. The presence of noise in data is a common problem that produces several negative consequences in classification problems. Noise is an unavoidable problem, which affects the data collection and data preparation processes in Data Mining applications, where errors commonly occur. The performance of the models built under such circumstances will heavily depend on the quality of the training data, but also on the robustness against the noise of the model learner itself. Hence, problems containing noise are complex problems and accurate solutions are often difficult to achieve without using specialized techniques—particularly if they are noise-sensitive. Identifying the noise is a complex task that will be developed in Sect. 5.1. Once the noise has been identified, the different kinds of such an imperfection are described in Sect. 5.2. From this point on, the two main approaches carried out in the literature are described. On the first hand, modifying and cleaning the data is studied in Sect. 5.3, whereas designing noise robust Machine Learning algorithms is tackled in Sect. 5.4. An empirical comparison between the latest approaches in the specialized literature is made in Sect. 5.5.