Entropy and Algorithm of Obtaining Decision Trees in a Way Approximated to the Natural Intelligence

Abstract

The classification of knowledge of a specified subject area is an actual task. The well-known methods of obtaining decision trees using entropy are not suitable for the classification of the subject area knowledge. So, a new algorithm of obtaining decision trees, whose way of obtaining is approximated to the natural intelligence, is suggested in the article. Here, the knowledge of a subject area is presented as a complex of answers to questions, which help to find the solution to a current task. The connection of entropy with the appearance of knowledge, the classification of previous knowledge, and the definitions used in decision trees are also analyzed in the article. The latter is necessary to compare the suggested algorithm approximated to the natural intelligence with the traditional method, using a small example. The article contains the analysis of solving a classification task for such a subject area as optimization methods.