This tutorial is the continuation of the one devoted to the induction of decision rules (Supervised rule induction - Software comparison). I have not included Knime in the comparison because it implements a method which is different compared with the other tools. Knime computes fuzzy rules. It wants that the target variable is continuous. That seems rather mysterious in the supervised learning context where the class attribute is usually discrete. I thought it was more appropriate to detail the implementation of the method in a tutorial that is exclusively devoted to the Knime rule learner (version 2.1.1).
Especially, it is important to detail the reason of the data preparation and the reading of the results. To have a reference, we compare the results with those provided by the rule induction tool proposed by Tanagra.
Scientific papers about the method are available on line.
Keywords: induction of rules, supervised learning, fuzzy rules
Components: SAMPLING, RULE INDUCTION, TEST
Tutorial: en_Tanagra_Induction_Regles_Floues_Knime.pdf
Dataset: iris2D.txt
References :
M.R. Berthold, « Mixed fuzzy rule formation », International Journal of Approximate Reasonning, 32, pp. 67-84, 2003.
T.R. Gabriel, M.R. Berthold, « Influence of fuzzy norms and other heuristics on mixed fuzzy rule formation », International Journal of Approximate Reasoning, 35, pp.195-202, 2004.
