RESUMEN DE TESIS DOCTORAL A Unified Methodology to Evaluate Supervised and Non-Supervised Classification Algorithms Una Metodología Unificada para la Evaluación de Algoritmos de Clasificación tanto Supervisados como No-Supervisados

There is presently no unified methodology that allows the evaluation of supervised or non-supervised classification algorithms. Supervised problems are evaluated through quality functions while non-supervised problems are evaluated through several structural indexes. In both cases a lot of useful information remains hidden or is not considered by the evaluation method, such as the quality of the sample or the structural change generated by the classification algorithm. This work proposes a unified methodology that can be used to evaluate both type of classification problems. This new methodology yields a larger amount of information to the evaluator regarding the quality of the initial sample, when it exists, and regarding the change produced by the classification algorithm in the case of non-supervised classification problems. It also offers the added possibility of making comparative evaluations with different algorithms