TY - GEN
T1 - Machine Learning and Symbolic Learning for the Recognition of Leukemia L1, L2 and L3
AU - Ochoa-Montiel, Rocio
AU - Sossa, Humberto
AU - Olague, Gustavo
AU - Sánchez-López, Carlos
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Evolutionary vision
KW - Leukemia recognition
KW - Symbolic learning
UR - http://www.scopus.com/inward/record.url?scp=85133023023&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-07750-0_33
DO - 10.1007/978-3-031-07750-0_33
M3 - Contribución a la conferencia
AN - SCOPUS:85133023023
SN - 9783031077494
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 360
EP - 369
BT - Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings
A2 - Vergara-Villegas, Osslan Osiris
A2 - Cruz-Sánchez, Vianey Guadalupe
A2 - Sossa-Azuela, Juan Humberto
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Martínez-Trinidad, José Francisco
A2 - Olvera-López, José Arturo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th Mexican Conference on Pattern Recognition, MCPR 2022
Y2 - 22 June 2022 through 25 June 2022
ER -
