Add to ORKGAn approach to construct a new classifier called an intu-itionistic fuzzy decision tree is presented. Well known benchmark data is used to analyze the performance of the classifier. The results are compared to some other popular classification algorithms. Finally, the classifier behavior is verified while solving a real-world classifi-cation problem
Classification as a data mining materiel is the process of assigning entities to an already defined ...
Abstract—One of the methods most commonly used for learning and classification is using decision tre...
This paper proposes a framework which consists of a novel fuzzy inference algorithm to generate fuzz...
The inductive learning methodology known as decision trees, concerns the ability to classify objects...
In this paper, a new method of fuzzy decision trees called soft decision trees (SDT) is presented. T...
International audienceIn inductive learning, to build decision trees is often arduous when there exi...
Decision trees are one of the most popular choices for learning and reasoning from feature-based exa...
Abstract:- Decision tree induction is one of common approaches for extracting knowledge from a sets ...
There is a lot of approaches for data classification problems resolving. The most significant data c...
A popular method in machine learning for supervised classification is a decision tree. In this work ...
Mainly understandable decision trees have been intended for perfect symbolic data. Conventional cris...
AbstractThe data-driven identification of fuzzy rule-based classifiers for high-dimensional problems...
Several fuzzy extensions of decision tree induction, which is an established machine-learning method...
Part 7: DecisionsInternational audienceIn this paper a new classification solution which joins C–Fuz...
This chapter considers the soft computing approach called fuzzy decision trees (FDT), a form of clas...
Classification as a data mining materiel is the process of assigning entities to an already defined ...
Abstract—One of the methods most commonly used for learning and classification is using decision tre...
This paper proposes a framework which consists of a novel fuzzy inference algorithm to generate fuzz...
The inductive learning methodology known as decision trees, concerns the ability to classify objects...
In this paper, a new method of fuzzy decision trees called soft decision trees (SDT) is presented. T...
International audienceIn inductive learning, to build decision trees is often arduous when there exi...
Decision trees are one of the most popular choices for learning and reasoning from feature-based exa...
Abstract:- Decision tree induction is one of common approaches for extracting knowledge from a sets ...
There is a lot of approaches for data classification problems resolving. The most significant data c...
A popular method in machine learning for supervised classification is a decision tree. In this work ...
Mainly understandable decision trees have been intended for perfect symbolic data. Conventional cris...
AbstractThe data-driven identification of fuzzy rule-based classifiers for high-dimensional problems...
Several fuzzy extensions of decision tree induction, which is an established machine-learning method...
Part 7: DecisionsInternational audienceIn this paper a new classification solution which joins C–Fuz...
This chapter considers the soft computing approach called fuzzy decision trees (FDT), a form of clas...
Classification as a data mining materiel is the process of assigning entities to an already defined ...
Abstract—One of the methods most commonly used for learning and classification is using decision tre...
This paper proposes a framework which consists of a novel fuzzy inference algorithm to generate fuzz...