TY - GEN
T1 - Typical Testor Selection Process for Classification Models
AU - Martínez-Mejía, Mateo
AU - Alba-Cabrera, Eduardo
AU - Pérez-Pérez, Noel
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - This work develops a process based on Testor Theory and new ideas related to this field that allows a ranking of typical testors based on the similarity between objects from the same class and the dissimilarity between objects from different classes. This process allows us to select the typical testors that will perform effectively to reduce the number of features in a dataset. We validate this process by examining the results obtained from a classification model for three different datasets, using the best and worst-ranked typical testors selected by the algorithm. Lastly, we analyze the results obtained and present the effectiveness of the method, as well as the advantages it brings.
AB - This work develops a process based on Testor Theory and new ideas related to this field that allows a ranking of typical testors based on the similarity between objects from the same class and the dissimilarity between objects from different classes. This process allows us to select the typical testors that will perform effectively to reduce the number of features in a dataset. We validate this process by examining the results obtained from a classification model for three different datasets, using the best and worst-ranked typical testors selected by the algorithm. Lastly, we analyze the results obtained and present the effectiveness of the method, as well as the advantages it brings.
KW - Classification model
KW - Dissimilarity
KW - Similarity
KW - Testor theory
KW - Typical testor
UR - http://www.scopus.com/inward/record.url?scp=85201085653&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-66431-1_36
DO - 10.1007/978-3-031-66431-1_36
M3 - Contribución a la conferencia
AN - SCOPUS:85201085653
SN - 9783031664304
T3 - Lecture Notes in Networks and Systems
SP - 512
EP - 524
BT - Intelligent Systems and Applications - Proceedings of the 2024 Intelligent Systems Conference IntelliSys Volume 3
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - Intelligent Systems Conference, IntelliSys 2024
Y2 - 5 September 2024 through 6 September 2024
ER -