SOLVING TWO-CLASS CLASSIFICATION PROBLEM USING MEMETIC PROGRAMMING | ||||
| Assiut University Journal of Multidisciplinary Scientific Research | ||||
| Volume 48, Issue 1, June 2019, Page 58-71 PDF (767.65 K) | ||||
| Document Type: Novel Research Articles | ||||
| DOI: 10.21608/aunj.2019.220870 | ||||
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| Abstract | ||||
| The main target of the two-class classification problem is to design a classifier that discriminates between two objects from a seen dataset, then use this classifier to predict the object’s class for unseen instances. Different methods have been used to solve the two-class classification problem, such as Genetic algorithm (GA) and Genetic Programming (GP). However, there is still a need to design new methods that can overcome some limitations in evolutionary algorithms, e.g., the high disruption of the breading operations; mutation and crossover. Recently, the Memetic Programming (MP) algorithm was proposed as an improvement to the GP algorithm. In this paper, we adapt the MP algorithm to produce a new classifier algorithm called the Memetic Programming Classifier (MPC) algorithm to solve the two-class classification problem. The performance of MPC is validated through different datasets from the UCI database and the accuracy is compared along with different methods. As a result, the proposed MPC algorithm shows a competitive performance compared with 179 classifiers in the literature. | ||||
| Keywords | ||||
| Classifier; Classification Problem, Genetic Programming; Local Search; Memetic Programming | ||||
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