A software implementation of the fuzzy rule learning algorithm NSLVOrd for ordinal classification into KEEL

Abstract

Ordinal classification can be used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where the relative ordering between different values is significant. Ordinal classification problems are getting an important position in learning problems with examples such as studies on food quality or credit risks related to the financial obligations of a company. KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) Java software tool that can be used for a large number of different knowledge data discovery tasks. It contains a wide variety of computational intelligence algorithm implementations providing a good tool and scenario to assess and develop computational problems. Focusing on problems of nominal classification or regression, there is not a wide variety of software that addresses this type of problems. This work aims to facilitate the use of the fuzzy rule learning algorithm for ordinal classification (NSLVOrd) enabling its integration into the well-known software tool KEEL. The implementation and some instructions to execute NSLVOrd in KEEL are also detailed showing the ease of use for any KEEL user.