Automation of a Decision Tree Conversion into a Fuzzy Inference System Using ANTLR
S. S. Sosinskaya, Roman S. Dorofeev, Andrey S. Dorofeev, T.R. Usenko · 2020
The paper discusses techniques of processing a sample of sets of numerical features of observations that relate to a certain subject area and belong to certain classes. Such techniques include well-known methods of constructing a decision tree and a fuzzy inference system. An isomorphism of the decision trees and a corresponding fuzzy inference system rule set is being justified. Algorithms of both methods are described in special languages. An approach to automated conversion of the decision tree to a fuzzy inference system using ANTLR, a tool for creating compilers, is proposed. The toolkit used, along with the creation of classes for lexical and parsing of a description in one language, allows generate a class for converting text from one language to another. The relevance of the approach is that with representing fuzzy classifying knowledge allows one to implement an expert system, allowing domain specialists to classify objects. Usage of the decision trees in expert systems is problematic. An example of applying this approach to a classification of leaf specimens originating from different plant species is given.