Um estudo comparativo entre algoritmos indutivos incrementais e não incrementais utilizados na geração de bases de conhecimento a partir de exemplos.

Abstract

The critical phase of the process of building expert systems is the responsible for generating the knowledge base because she is expensive difficult and greatly influences the final quality of these systems. Induction from examples generates knowledge bases in decision trees or rules through algorithms called inductive algorithms. Inductive algorithms can be non-incremental or incremental algorithms. Non-incremental algorithms are input a set of examples and output as a knowledge base. Incremental algorithms receive as input a database of knowledge and a set of new examples. updated knowledge base. In this paper we present a comparative performance analysis between these two families of decision tree generating algorithms. We show through theoretical and experimental comparative analysis that, contrary to what the authors of the incremental algorithms said these algorithms, only in rare domain and domain situations. number of examples these algorithms were more efficient non-incremental, thus not meeting the objectives proposed.