A first approach for cost-sensitive classification with linguistic Genetic Fuzzy Systems in imbalanced data-sets

Classification in imbalanced domains has become one of the most relevant problems within the area of Machine Learning at the present. This problem has raised in significance due to its presence in many real applications and it occurs when the distribution of the available examples to carry out the learning process is very different between the classes (often for binary class data-sets). Usually, the underrepresented class is the concept of the most interest for the problem, being the cost derived from a misclassification of these examples much higher than that of the remaining examples. In this work we analyze the behaviour of a cost-sensitive learning method for Fuzzy Rule Based Classification Systems in the scenario of high imbalanced data-sets. Specifically, we focus on one representative rule learning approach for Genetic Fuzzy Systems, the Fuzzy Hybrid Genetics-Based Machine Learning algorithm. The experimental results show how our cost-sensitive approach in this type of domains will help us to obtain very accurate solutions in shorter training times and also with a lower complexity with respect to other possibilities proposed for classification with imbalanced problems such as the use of preprocessing to rebalance the class distribution.