| Resumen |
Leukemia is a health problem that affects to world population causing thousands of kills yearly, thus accurate and human-readable diagnostic methods are required. Symbolic learning uses methods based on high-level representations of problems, which is useful to design interpretable models to understand the solutions found to solve a problem. In this work, we analyze the performance of 3 classifiers used frequently in machine learning, which are independently embedded into a model of symbolic learning named brain programming. Results suggest that the classifiers as MLP and SVM are robust to noisy data, with the MLP demonstrating the most stable behavior into the symbolic learning model, which is fundamental in models of evolutionary vision as the brain programming. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
