Add to ORKGAbstract. Decision trees are considered as an efficient technique to express classification knowledge and to use it. However, their most standard algorithms do not deal with uncertainty, especially the cognitive one. In this paper, we develop a method to adapt the decision tree technique to the case where the object’s classes are not exactly known, and where the uncertainty about the class ’ value is represented by a belief function. The adaptation concerns both the construction of the tree and its use to classify new objects characterized by uncertain attribute values.
Decision trees estimate prediction certainty using the class distribution in the leaf responsible fo...
The aim of the article is to analyse and thoroughly research the methods of construction of the deci...
Traditional decision tree classifiers work with data whose values are known and precise. We extend s...
Decision trees are considered as an efficient technique to express classification knowledge and to u...
International audienceDecision trees are regarded as convenient machine learning techniques for solv...
AbstractThis paper extends the decision tree technique to an uncertain environment where the uncerta...
This paper extends the decision tree technique to an uncertain environment where the uncertainty is ...
One of the current challenges in the field of data mining is to develop techniques to analyze uncert...
The belief decision tree approach is a decision tree method adapted in order to handle uncertainty...
The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the unc...
AbstractThis paper addresses the classification problem with imperfect data. More precisely, it exte...
As well-known machine learning methods, decision trees are widely applied in classification and reco...
AbstractThe belief decision tree (BDT) approach is a decision tree in an uncertain environment where...
Abstract — Classification is one of the important data mining techniques and Decision Tree is a most...
Certain data is a data whose values are known precisely whereas uncertain data means whose value are...
Decision trees estimate prediction certainty using the class distribution in the leaf responsible fo...
The aim of the article is to analyse and thoroughly research the methods of construction of the deci...
Traditional decision tree classifiers work with data whose values are known and precise. We extend s...
Decision trees are considered as an efficient technique to express classification knowledge and to u...
International audienceDecision trees are regarded as convenient machine learning techniques for solv...
AbstractThis paper extends the decision tree technique to an uncertain environment where the uncerta...
This paper extends the decision tree technique to an uncertain environment where the uncertainty is ...
One of the current challenges in the field of data mining is to develop techniques to analyze uncert...
The belief decision tree approach is a decision tree method adapted in order to handle uncertainty...
The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the unc...
AbstractThis paper addresses the classification problem with imperfect data. More precisely, it exte...
As well-known machine learning methods, decision trees are widely applied in classification and reco...
AbstractThe belief decision tree (BDT) approach is a decision tree in an uncertain environment where...
Abstract — Classification is one of the important data mining techniques and Decision Tree is a most...
Certain data is a data whose values are known precisely whereas uncertain data means whose value are...
Decision trees estimate prediction certainty using the class distribution in the leaf responsible fo...
The aim of the article is to analyse and thoroughly research the methods of construction of the deci...
Traditional decision tree classifiers work with data whose values are known and precise. We extend s...