Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose automatic generation methods of fuzzy classification rules with the Genetic Algorithms (GAs) to obtain compact fuzzy systems. This time,  propose an approach of hyper-cone membership function to construct rules for the antecedent part. Then, this method is used to determine the location and shape of hyper-cone membership function in the antecedent part, output class and the number of necessary inputs of each rule by GAs. Also, using the rule addition method in GA process, compact fuzzy classification systems are obtained. Though the proposed methods are quite simple, the process of GAs on both methods presents a solution for solving two-objective optimization problems: increasing the numbers of correct pattern classification, while decreasing the rule and input numbers optimally. This method was applied to Wine data sets and Wisconsin Prognostic Breast Cancer (WPBC) data sets. Wine data sets consist of 13 inputs and three outputs, while WPBC data sets contain 33 inputs and two outputs.
['Lina Hng', 'Kenji Miyasaka', 'Hiroyuki Inoue', 'Mitsuru Tsukamoto']
I am writing a research paper on a idea Automatic generation of fuzzy classification systems using hyper-cone membership functions
[{"from": "human", "value": "I am writing a research paper on a idea Automatic generation of fuzzy classification systems using hyper-cone membership functions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose automatic generation methods of fuzzy classification rules with the Genetic Algorithms (GAs) to obtain compact fuzzy systems. This time,  propose an approach of hyper-cone membership function to construct rules for the antecedent part. Then, this method is used to determine the location and shape of hyper-cone membership function in the antecedent part, output class and the number of necessary inputs of each rule by GAs. Also, using the rule addition method in GA process, compact fuzzy classification systems are obtained. Though the proposed methods are quite simple, the process of GAs on both methods presents a solution for solving two-objective optimization problems: increasing the numbers of correct pattern classification, while decreasing the rule and input numbers optimally. This method was applied to Wine data sets and Wisconsin Prognostic Breast Cancer (WPBC) data sets. Wine data sets consist of 13 inputs and three outputs, while WPBC data sets contain 33 inputs and two outputs."}]