A Genetic Programming Approach to Binary Classification Problem

The Binary classification is the most challenging problem in machine learning. One of the most promising technique to solvethis problem is by implementing genetic programming (GP). GP is one of Evolutionary Algorithm (EA) that used to solveproblems that humans do not know how to solve it directly. T...

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Bibliographic Details
Published inEAI endorsed transactions on energy web Vol. 8; no. 31; p. 165523
Main Authors Santoso, Leo, Singh, Bhopendra, Rajest, S., Regin, R., Kadhim, Karrar
Format Journal Article
LanguageEnglish
Published European Alliance for Innovation (EAI) 2021
Subjects
binary classification
evolutionary algorithms
genetic programming
machine learning
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Summary:The Binary classification is the most challenging problem in machine learning. One of the most promising technique to solvethis problem is by implementing genetic programming (GP). GP is one of Evolutionary Algorithm (EA) that used to solveproblems that humans do not know how to solve it directly. The objectives of this research is to demonstrate the use ofgenetic programming in this type of problems; that is, other types of techniques are typically used, e.g., regression, artificialneural networks. Genetic programming presents an advantage compared to those techniques, which is that it does not needan a priori definition of its structure. The algorithm evolves automatically until finding a model that best fits a set of trainingdata. Feature engineering was considered to improve the accuracy. In this research, feature transformation and featurecreation were implemented. Thus, genetic programming can be considered as an alternative option for the development ofintelligent systems mainly in the pattern recognition field.
ISSN:2032-944X
2032-944X
DOI:10.4108/eai.13-7-2018.165523