ew 18: e26

Research Article

A Genetic Programming Approach to Binary Classification Problem

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  • @ARTICLE{10.4108/eai.13-7-2018.165523,
        author={Leo Willyanto Santoso and Bhopendra Singh and S. Suman Rajest and R. Regin and Karrar Hameed Kadhim},
        title={A Genetic Programming Approach to Binary Classification Problem},
        journal={EAI Endorsed Transactions on Energy Web: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EW},
        year={2020},
        month={7},
        keywords={binary classification, evolutionary algorithms, genetic programming, machine learning},
        doi={10.4108/eai.13-7-2018.165523}
    }
    
  • Leo Willyanto Santoso
    Bhopendra Singh
    S. Suman Rajest
    R. Regin
    Karrar Hameed Kadhim
    Year: 2020
    A Genetic Programming Approach to Binary Classification Problem
    EW
    EAI
    DOI: 10.4108/eai.13-7-2018.165523
Leo Willyanto Santoso1,*, Bhopendra Singh2, S. Suman Rajest3, R. Regin4, Karrar Hameed Kadhim5
  • 1: Petra Christian University, 121-131 Siwalankerto Rd, Surabaya, East Java, Indonesia
  • 2: Associate Professor, Amity University, Dubai
  • 3: Vels Institute of Science, Technology & Advanced Studies (VISTAS), Tamil Nadu, India
  • 4: Assistant Professor, Department of Information Technology, Adhiyamaan College of Engineering, India
  • 5: AL-Musaib Technical College, AL-Furat Al-Awsat Technical University, Iraq
*Contact email: leow@petra.ac.id

Abstract

The Binary classification is the most challenging problem in machine learning. One of the most promising technique to solve this problem is by implementing genetic programming (GP). GP is one of Evolutionary Algorithm (EA) that used to solve problems that humans do not know how to solve it directly. The objectives of this research is to demonstrate the use of genetic programming in this type of problems; that is, other types of techniques are typically used, e.g., regression, artificial neural networks. Genetic programming presents an advantage compared to those techniques, which is that it does not need an a priori definition of its structure. The algorithm evolves automatically until finding a model that best fits a set of training data. Feature engineering was considered to improve the accuracy. In this research, feature transformation and feature creation were implemented. Thus, genetic programming can be considered as an alternative option for the development of intelligent systems mainly in the pattern recognition field.