A complete fuzzy decision tree technique

By Cristina Olaru, Louis Wehenkel. Fuzzy Sets Syst., 2003

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Zero-shot recognition with unreliable attributes (2014, background): “Similar concepts are explored in [28] to handle features represented as discrete distributions and in [16] to propagate instances with soft node memberships.”
Understanding Random Forests: From Theory to Practice (2014, background): “Finally, in an another direction, several authors [Adamo, 1980; Yuan and Shaw, 1995; Olaru and Wehenkel, 2003] propose in fuzzy decision trees to redefine the notion of split as overlapping fuzzy sets (instead of disjoint subsets), hence allowing samples close to the decision threshold v to propagat”
Learning fuzzy decision trees using integer programming (2018, methodology): “Existing fuzzy classifiers such as [7], [13], [14], [15] typically use heuristics to construct decision trees.”
Classification of Cancer Data: Analyzing Gene Expression Data Using a Fuzzy Decision Tree Algorithm (2018, background): “Soft Decision Trees (SDT) are presented in (Olaru & Wehenkel, 2003), which combine tree-growing and pruning to determine the structure and refitting and backfitting to improve the generalization capability.”
Closed-form dual perturb and combine for tree-based models (2005, methodology): “In a fuzzy decision tree, the weight used to propagate an instance to the left and right successors of a test node is interpreted as a fuzzy set membership degree (see (Olaru & Wehenkel, 2003) and the references therein).”