Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This contribution proposes a genetic learning process for designing the knowledge base of Fuzzy Rule-Based classification Systems, that will be used as binary classifiers in a One-vs-One decomposition for multi-class problems. A Genetic Algorithm is designed to adapt the number of fuzzy labels per variable (granularity level) for each classifier in order to improve the accuracy rate of a multi-class classifier. The genetic learning process evolves granularity levels and needs a fuzzy rules generation method for generating the whole knowledge base of the Fuzzy System. Several data-sets from KEEL data-set repository are used in the experimental study and  compare our proposal with three related methods: the standard way to design Fuzzy Rule-Based Classification Systems using the fuzzy rules generation method chosen with and without One-vs-One decomposition, and our proposal of genetic granularity level learning without One-vs-One decomposition.
['Pedro Caballero Villar', 'Alberto Fernández', 'Rosana Montes', 'Ana M. Sánchez', 'Francisco Herreraz']
I am writing a research paper on a idea Improving the OVO performance in Fuzzy Rule-Based Classification Systems by the genetic learning of the granularity level
[{"from": "human", "value": "I am writing a research paper on a idea Improving the OVO performance in Fuzzy Rule-Based Classification Systems by the genetic learning of the granularity level"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This contribution proposes a genetic learning process for designing the knowledge base of Fuzzy Rule-Based classification Systems, that will be used as binary classifiers in a One-vs-One decomposition for multi-class problems. A Genetic Algorithm is designed to adapt the number of fuzzy labels per variable (granularity level) for each classifier in order to improve the accuracy rate of a multi-class classifier. The genetic learning process evolves granularity levels and needs a fuzzy rules generation method for generating the whole knowledge base of the Fuzzy System. Several data-sets from KEEL data-set repository are used in the experimental study and  compare our proposal with three related methods: the standard way to design Fuzzy Rule-Based Classification Systems using the fuzzy rules generation method chosen with and without One-vs-One decomposition, and our proposal of genetic granularity level learning without One-vs-One decomposition."}]