Abstract
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.
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Acknowledgements
Authors would like to acknowledge the support provided by the Instituto Politécnico Nacional under projects: SIP 20200630, SIP 20210788, and SIP 20220226; CONACYT under projects: 65 (Fronteras de la Ciencia) and 6005 (FORDECYT-PRONACES), and CICESE through the project 634-135 to carry out this research. First author thanks the Autonomous University of Tlaxcala, Mexico for the support. Authors also express their gratitude to the Applied Computational Intelligence Network (RedICA).
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Ochoa-Montiel, R., Sossa, H., Olague, G., Sánchez-López, C. (2022). Machine Learning and Symbolic Learning for the Recognition of Leukemia L1, L2 and L3. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_33
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