Discretization refers to splitting the range of continuous values into intervals so as to provide useful information about classes. This is usually done by minimizing a goodness m...
In discretization of a continuous variable its numerical value range is divided into a few intervals that are used in classification. For example, Na¨ıve Bayes can benefit from...
Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This pa...
This paper extends the Boltzmann Selection, a method in EDA with theoretical importance, from discrete domain to the continuous one. The difficulty of estimating the exact Boltzma...
The proposed feature selection method aims to find a minimum subset of the most informative variables for classification/regression by efficiently approximating the Markov Blanket ...