J Biomed Inform - Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions.

Tópicos

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Resumo

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.

Resumo Limpo

work present novel applic time seri discret use evolutionari program classif precancer cervic lesion approach optim number interv length amplitud time seri compress preserv import inform classif purpos use evolutionari program search good discret scheme guid cost function consid three criteria entropi regard classif complex measur number differ string need repres complet data set compress rate assess length discret represent discret approach evalu use time seri data base tempor pattern observ classic test use cervic cancer detect classif accuraci reach method compar wellknown time seri discret algorithm sax dimension reduct method pca statist analysi classif accuraci show discret represent effici complet raw represent present applic reduc dimension time seri length represent also competit term classif accuraci compar similar approach

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