Soft Genetic Programming Binary Classifiers
This work addresses the complexity and customization issues of GP classifiers for binary classification tasks, though it appears incremental as it builds on existing GP concepts.
The paper tackles the limited use of genetic programming (GP) for binary classification by introducing a 'soft' genetic programming (SGP) method that makes logical operator trees more flexible to find dependencies in datasets, resulting in promising test outcomes.
The study of the classifier's design and it's usage is one of the most important machine learning areas. With the development of automatic machine learning methods, various approaches are used to build a robust classifier model. Due to some difficult implementation and customization complexity, genetic programming (GP) methods are not often used to construct classifiers. GP classifiers have several limitations and disadvantages. However, the concept of "soft" genetic programming (SGP) has been developed, which allows the logical operator tree to be more flexible and find dependencies in datasets, which gives promising results in most cases. This article discusses a method for constructing binary classifiers using the SGP technique. The test results are presented. Source code - https://github.com/survexman/sgp_classifier.