An artificial immune system for fuzzy-rule induction in data mining

This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm

Modernización del reflector de 76 cm. de la Estación Astronómica Dr. Carlos Ulrico Cesco

Se describen los trabajos realizados para corregir defectos y modernizar el telescopio reflector de 76 cm. a fin de posibilitar el uso de sensores tipo CCD en imagen directa. También se detallan los trabajos proyectados para el futuro con el objeto de optimizar el uso del instrumento.Asociación Argentina de Astronomí