The 1960s included the earliest discussion on an inductive approach towards the construction of a decision tree for the classification of objects that are described by attributes (Hunt, Marin, & Stone, 1966). It is not until over a decade later that the popularization of the decision tree method was materialized with the introduction of what developed into the Interactive Dichotomizer 3 (Quinla...

This paper proposes a new methodology for designing Fuzzy Cognitive Maps using crisp decision trees that have been fuzzified. Fuzzy cognitive map is a knowledge-based technique that works as an artificial cognitive network inheriting the main aspects of cognitive maps and artificial neural networks. Decision trees, in the other hand, are well known intelligent techniques that extract rules from...

In this paper, we propose a novel fuzzy expert system for detection of Coronary Artery Disease, using cuckoo search algorithm. This system includes three phases: firstly, at the stage of fuzzy system design, a decision tree is used to extract if-then rules which provide the crisp rules required for Coronary Artery Disease detection. Secondly, the fuzzy system is formed by setting the intervals ...

This paper is focused on an introduction of a new software product, which is called FuzzME. This software was developed as a tool for creating fuzzy models of multiple-criteria evaluation and decision making. The type of evaluations employed in the fuzzy models fully corresponds with the paradigm of the fuzzy set theory; the evaluations express the (fuzzy) degrees of fulfillment of correspondin...

In this paper, we present the discovery of rules for different challenges encountered in video indexing. These rules should be considered as knowledge that can be used as a guideline for the development of better indexing tools. We use a fuzzy decision tree to extract the rules based on color proportions of key-frames extracted from one single video-news. Experimental results and comparisons wi...

Abstract Accurate and less invasive personalized predictive medicine can spare many breast cancer patients from receiving complex surgical biopsies, unnecessary adjuvant treatments and its expensive medical cost. Cancer prognosis estimates recurrence of disease and predict survival of patient; hence resulting in improved patient management. To develop such knowledge based prognostic system, thi...

Mainly understandable decision trees have been intended for perfect symbolic data. Conventional crisp decision trees (DT) are extensively used for classification purpose. However, there are still many issues particularly when we used the numerical (continuous valued) attributes. Structured continuouslabel classification is one type of classification in which the label is continuous in the data....

Decision Tree Induction (DTI), one of the Data Mining classification methods, is used in this research for predictive problem solving in analyzing patient medical track records. In this paper, we extend the concept of DTI dealing with meaningful fuzzy labels in order to express human knowledge for mining fuzzy association rules. Meaningful fuzzy labels (using fuzzy sets) can be defined for each...

Warehouse location selection is a multi-criteria decision problem including both quantitative and qualitative criteria and has a strategic importance for many companies. The conventional methods for warehouse location selection are inadequate for dealing with the imprecise or vague nature of linguistic assessment. To overcome this difficulty, fuzzy multi-criteria decision-making methods are pro...

Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore t...