University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 4 1 2013 05 22 Using CFO method to design a novel multi-objective classifier Using CFO method to design a novel multi-objective classifier 43 56 15354 FA S. Sheikhpour Department of Electrical Engineering, University of Birjand, Birjand, Iran S.h. Zahiri Department of Electrical Engineering, University of Birjand, Birjand, Iran Journal Article 2016 06 14 In designing a classifier and estimating the optimum decision hyperplanes, the main goal is often high “recognition score” in the training phase. While other objectives such as "reliability" of decisions and "the number of optimum decision functions" are also very important factors during designing classifiers, which should never be forgotten. In this paper, at first, the central force optimization (CFO) algorithm developed for multi-objective optimization problems (namely, MOCFO), and then a novel multi-objective classifier is constructed based on using the proposed MOCFO and called MOCFO-classifier. In fact, MOCFO-classifier optimizes the above objectives simultaneously. Due to selecting the number of optimal hyperplane in the proposed method, the important issues "overfitting" and "overtraining" have also been removed. The experimental results on difficult test data show that the proposed multi-objective classifier provides a set of various and optimum options of the hyperplane that separate different classes for making user ideal conditions in regards to selecting mentioned aspects. In designing a classifier and estimating the optimum decision hyperplanes, the main goal is often high “recognition score” in the training phase. While other objectives such as "reliability" of decisions and "the number of optimum decision functions" are also very important factors during designing classifiers, which should never be forgotten. In this paper, at first, the central force optimization (CFO) algorithm developed for multi-objective optimization problems (namely, MOCFO), and then a novel multi-objective classifier is constructed based on using the proposed MOCFO and called MOCFO-classifier. In fact, MOCFO-classifier optimizes the above objectives simultaneously. Due to selecting the number of optimal hyperplane in the proposed method, the important issues "overfitting" and "overtraining" have also been removed. The experimental results on difficult test data show that the proposed multi-objective classifier provides a set of various and optimum options of the hyperplane that separate different classes for making user ideal conditions in regards to selecting mentioned aspects. https://isee.ui.ac.ir/article_15354_03a6b7aadf5e5aa31c10c9a11db175dc.pdf