The data-driven identification of fuzzy rule-based classifiers for high-dimensional problems is addressed. A binary decision-tree-based initialization of fuzzy classifiers is proposed for the selection of the relevant features and effective initial partitioning of the input domains of the fuzzy system. Fuzzy classifiers have more flexible decision boundaries than decision trees (DTs) and can th...

Decision trees have been successfully applied to many areas for tasks such as classi cation, regression, and feature subset selection. Decision trees are popular models in machine learning due to the fact that they produce graphical models, as well as text rules, that end users can easily understand. Moreover, their induction process is usually fast, requiring low computational resources. Fuzzy...

The study problem was learning a fuzzy decision tree to classify patients with adnexal mass into either of benign or malignant class prior to surgery using patients’ medical history, physical exam, laboratory tests, and ultrasonography. A learning algorithm was developed to learn a fuzzy decision tree in three steps. In the growing step, a binary decision tree was learned from a dataset of pati...

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common s...

Classic inverse minimum spanning tree problem is to make the least edge weight modification such that a predetermined spanning tree is a minimum spanning tree with respect to the new edge weights. Although many applications can be formulated into this problem, fuzzy parameters are met in some real-world applications. In this paper, a type of inverse minimum spanning tree problem with fuzzy edge...

Apart from the need for superior accuracy, healthcare applications of intelligent systems also demand deployment interpretable machine learning models which allow clinicians to interrogate and validate extracted medical knowledge. Fuzzy rule-based are generally considered that able reflect associations between conditions associated symptoms, through use linguistic if-then statements. Systems bu...

Inductive learning that creates decision trees from a set of existing cases is useful for automated knowledge acquisition. Most of the existing methods in literature are based on crisp concepts that are weak in handling marginal cases. In this paper, we present a fuzzy inductive learning method that integrates the fuzzy set theory into the regular inductive learning processes. The method conver...