Improving Fuzzy Rule Based Classification Systems in Big Data via Support-based Filtering
Luis Íñiguez, Mikel Galar, Alberto Fernández · 2018
Fuzzy Rule Based Classification Systems have the benefit of making possible to understand the decision of the classifier. Additionally, they have shown to be robust to solve complex problems. When these capabilities are applied to the context of Big Data, the benefits get multiplied.Therefore, to achieve the highest advantages of Fuzzy Rule Based Classification Systems, the output model must be both interpretable and accurate. The former is achieved by using fuzzy linguistic labels, that are related to human understanding. The latter is achieved by means of robust fuzzy rules, which are identified by means of a component known as "fuzzy rule weight". However, obtaining these rule weights is computationally expensive, resulting on a bottle-neck when applied in Big Data problems.In this work, we propose Chi-BD-SF, which stands for Chi Big Data Support Filtering. It comprises a scalable yet accurate fuzzy rule learning algorithm. It is based on the well-known Chi et al., exchanging the rule weight computation by a support metric in order to solve the conflicts between different consequent rules. In order to show the goodness of this proposal, we analyze several performance metrics, such as the quality of classification, the robustness of the rule base generated and the runtimes of the usage of traditional weights and the support of the rule. The results of our novel Chi-BD-SF approach, in contrast to related Big Data fuzzy classifiers, show that this proposal is able to out-speed the usage of rule weights also obtaining more accurate results.