title: Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions.
abstract: In this work, we present a novel application of time series discretization using evolutionary programming for the classification of precancerous cervical lesions. The approach optimizes the number of intervals in which the length and amplitude of the time series should be compressed, preserving the important information for classification purposes. Using evolutionary programming, the search for a good discretization scheme is guided by a cost function which considers three criteria: the entropy regarding the classification, the complexity measured as the number of different strings needed to represent the complete data set, and the compression rate assessed as the length of the discrete representation. This discretization approach is evaluated using a time series data based on temporal patterns observed during a classical test used in cervical cancer detection; the classification accuracy reached by our method is compared with the well-known times series discretization algorithm SAX and the dimensionality reduction method PCA. Statistical analysis of the classification accuracy shows that the discrete representation is as efficient as the complete raw representation for the present application, reducing the dimensionality of the time series length by 97%. This representation is also very competitive in terms of classification accuracy when compared with similar approaches.
authors.affiliation: School of Physics and Artificial Intelligence, Department of Artificial Intelligence, Universidad Veracruzana, Sebastián Camacho # 5, 91000 Xalapa, Veracruz, Mexico. Electronic address: heacosta@uv.mx.
authors.forename: Héctor-Gabriel
authors.initials: HG
authors.lastname: Acosta-Mesa
authors.affiliation: School of Physics and Artificial Intelligence, Department of Artificial Intelligence, Universidad Veracruzana, Sebastián Camacho # 5, 91000 Xalapa, Veracruz, Mexico. Electronic address: frechyr@hotmail.com.
authors.forename: Fernando
authors.initials: F
authors.lastname: Rechy-Ramírez
authors.affiliation: School of Physics and Artificial Intelligence, Department of Artificial Intelligence, Universidad Veracruzana, Sebastián Camacho # 5, 91000 Xalapa, Veracruz, Mexico. Electronic address: emezura@uv.mx.
authors.forename: Efrén
authors.initials: E
authors.lastname: Mezura-Montes
authors.affiliation: School of Physics and Artificial Intelligence, Department of Artificial Intelligence, Universidad Veracruzana, Sebastián Camacho # 5, 91000 Xalapa, Veracruz, Mexico. Electronic address: ncruz@uv.mx.
authors.forename: Nicandro
authors.initials: N
authors.lastname: Cruz-Ramírez
authors.affiliation: Obstetrician and Gynaecologist, Diego Leño # 22, C.P. 91000 Xalapa, Veracruz, Mexico. Electronic address: roheji@msn.com.
authors.forename: Rodolfo
authors.initials: R
authors.lastname: Hernández Jiménez
mesh_terms: D005260:Female; D006801:Humans; D011230:Precancerous Conditions / Q000145:classification*; D025341:Principal Component Analysis; D002583:Uterine Cervical Neoplasms / Q000145:classification*
keywords: Cervical cancer detection; Classification; Evolutionary algorithms; Times series discretization