| Resumen |
The presence of machine learning, data mining and related disciplines is increasingly ev-ident in everyday environments. The support for the applications of learning techniques in topics related to economic risk assessment, among other financial topics of interest, is relevant for us as human beings. The content of this paper consists of a proposal of a new supervised learning algorithm and its application in real world datasets related to finance, called D1-NN (Dynamic 1-Nearest Neighbor). The D1-NN performance is competitive against the main state of the art algorithms in solving finance-related problems. The effectiveness of the new D1-NN classifier was compared against five supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, classifier ensembles, and neural networks), with superior results overall. |
