Listar Artículo en revista de investigación por autor "0000-0003-1053-4658"
Mostrando ítems 1-6 de 6
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A regression model based on the nearest centroid neighborhood
Garcia, Vicente (2018-04-01)The renowned k-nearest neighbor decision rule is widely used for classification tasks, where the label of any new sample is estimated based on a similarity criterion defined by an appropriate distance function. It has also ... -
Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction
García, Vicente (2019-03-01)Bankruptcy prediction has acquired great relevance for financial institutions due to the complexity of global economies and the growing number of corporate failures, especially since the world financial crisis of 2008. In ... -
Estudio empírico del enfoque asociativo en el contexto de los problemas de clasificación
Sánchez, Laura Cleofas (2019). Investigaciones realizadas por la comunidad cient´ıfica han evidenciado que el rendimiento de los clasificadores, no solamente depende de la regla de aprendizaje, sino tambien de las complejidades inher- ´ entes en ... -
Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction
García, Vicente (2019-05-01)Credit risk and corporate bankruptcy prediction has widely been studied as a binary classification problem using both advanced statistical and machine learning models. Ensembles of classifiers have demonstrated their ... -
Gene selection and disease prediction from gene expression data using a two-stage hetero-associative memory
Cleofas-Sánchez, Laura (2019-04-01)In general, gene expression microarrays consist of a vast number of genes and very few samples, which represents a critical challenge for disease prediction and diagnosis. This paper develops a two-stage algorithm that ... -
Understanding the apparent superiority of over-sampling through an analysis of local information for class-imbalanced data
García, Vicente (2019-10-14)Data plays a key role in the design of expert and intelligent systems and therefore, data preprocessing appears to be a critical step to produce high-quality data and build accurate machine learning models. Over the past ...