A generalized fuzzy ID3 algorithm using generalized information entropy

A fuzzy decision tree is an important tool for knowledge acquisition in uncertain environments. Most of the existing fuzzy decision tree algorithms do not systematically consider the impact of the non-linear characteristics of the membership degree of fuzzy sets; they are therefore unable to integrate uncertainty processing preferences into the selection of extended attributes. This paper initially offers a generalized Hartley metric model and calculation method. We then introduce a fuzzy consciousness function and further provide generalized fuzzy partition entropy for the attribute-selecting heuristic of a fuzzy decision tree. We subsequently propose a generalized fuzzy partition entropy-based fuzzy ID3 algorithm (abbreviated as GFID3) that can support decision making and analyze the performance of the GFID3 through several case-based examples. The experimental results show that the GFID3 algorithm demonstrates better structural characteristics and operability in practical applications and has high computational precision. It ameliorates the deficiencies of existing fuzzy decision tree algorithms and can be used in fields such as complex systems optimization, data mining and intelligent systems.