New Filter method for categorical variables’ selection

It is worth noting that the variable-selection process has become an increasingly exciting challenge, given the dramatic increase in the size of databases and the number of variables to be explored and modelized. Therefore, several strategies and methods have been developed with the aim of selecting the minimum number of variables while preserving as much information for the interest variable of the system to be modelized (variable to predict). In this work, we will present a novel Filter method useful for selecting variables, distinct for its joint application of both simple as well as multivariate analyses to select variables. In the first place, we will deal with the major prevailing strategies and methods already underway. Secondly, we will expose our new method and establish a comparison of its achieved results with those of the existing methods. The experiments have been implemented on two different databases, namely, a cardiac diagnosis disease labeled "Spect Heart", and a car diagnosis, called "Car Diagnosis 2". As for the ultimate section, it will bear the conclusion as well some highlights for future research perspectives and potential horizons.