Proposal for a Statistical Reduct Method for Decision Tables
Yuichi Kato, Tetsuro Saeki, Shoutarou Mizuno · Lecture notes in computer science · 2015
Rough Sets theory is widely used as a method for estimating and/or inducing the knowledge structure of if-then rules from a decision table after a reduct of the table. The concept of the reduct is that of constructing the decision table by necessary and sufficient condition attributes to induce the rules. This paper retests the reduct by the conventional methods by the use of simulation datasets after summarizing the reduct briefly and points out several problems of their methods. Then a new reduct method based on a statistical viewpoint is proposed. The validity and usefulness of the method is confirmed by applying it to the simulation datasets and a UCI dataset. Particularly, this paper shows a statistical local reduct method, very useful for estimating if-then rules hidden behind the decision table of interest. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.