Research Article
A Genetic Programming Approach to Binary Classification Problem
@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}, volume={8}, number={31}, 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
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.
Copyright © 2020 Leo Willyanto Santoso et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
