This dissertation approaches the algorithm of Selection of Variables under Mutual Information with Uniform Distribution (MIFS-U) and presents an alternative method for estimate entropy and mutual information, measures that constitute the base of this selection algorithm. This method has, for foundation, the Cauchy-Schwartz quadratic mutual information and the quadratic Rényi entropy, combined, in the case of continuous variables, with Parzen Window density estimation. Experiments were accomplished with real public domain data, being such method compared with other, broadly used, that adopts the Shannon entropy definition and makes use, in the case of continuous variables, of the histogram density estimator The results show small variations among the two methods, what suggests a future investigation through a classifier, such as Neural Networks, to evaluate this results, qualitatively, in the light of the final objective that consists of the biggest sort exactness.