Detection of Rare Events Within Industrial Datasets by Means of Data Resampling and Specific Algorithms
In this paper, the authors deal with the problem of the detection of rare patterns in unbalanced datasets coming from the industrial world. Such kind of patterns usually corresponds to not frequent but very relevant events, such as the occurrence of product defects and machine faults. Within this paper, several approaches have been tested for the development of classifiers whose performance is able to meet the industrial requirements, i.e. a high rate of recognition of unfrequented patterns.