Add to ORKGAn SE-tree based Characterization of the Induction Problem Many induction programs use decision trees both as the basis for their search, and as a representation of their classifier solution. In this paper we propose a new structure, called SE-tree, as a more general alternative
Abstract. In the paper, a new method for cost-sensitive learning of decision trees is proposed. Our ...
We discuss an approach to constructing composite features during the induction of decision trees. Th...
Abstract. Decision tree learning represents a well known family of inductive learning algo-rithms th...
Many induction programs use decision trees both as the basis for their search, and as a representati...
Many induction programs use decision trees both as the basis for their search, and as a representati...
As a classifier, a Set Enumeration (SE) tree can be viewed as a generalization of decision trees. It...
Abstract. This paper addresses the issue of the decision tree induction. We treat this task as a sea...
The ability to restructure a decision tree efficiently enables a variety of approaches to decision t...
This paper introduces a new algorithm for the induction of decision trees, based on adaptive techniq...
Simple techniques for the development and use of decision tree classiers assume that all attribute v...
Decision tree induction is one of the most employed methods to extract knowledge from data, since th...
This paper presents a survey of evolutionary algorithms that are designed for decision-tree inductio...
As a classifier, a Set Enumeration (SE) tree can be viewed as a generalization of decision trees. We...
Part 2: AlgorithmsInternational audienceDecision trees are among the most popular classification alg...
This paper studies the effects on decision tree learning of constructing four types of attribute (co...
Abstract. In the paper, a new method for cost-sensitive learning of decision trees is proposed. Our ...
We discuss an approach to constructing composite features during the induction of decision trees. Th...
Abstract. Decision tree learning represents a well known family of inductive learning algo-rithms th...
Many induction programs use decision trees both as the basis for their search, and as a representati...
Many induction programs use decision trees both as the basis for their search, and as a representati...
As a classifier, a Set Enumeration (SE) tree can be viewed as a generalization of decision trees. It...
Abstract. This paper addresses the issue of the decision tree induction. We treat this task as a sea...
The ability to restructure a decision tree efficiently enables a variety of approaches to decision t...
This paper introduces a new algorithm for the induction of decision trees, based on adaptive techniq...
Simple techniques for the development and use of decision tree classiers assume that all attribute v...
Decision tree induction is one of the most employed methods to extract knowledge from data, since th...
This paper presents a survey of evolutionary algorithms that are designed for decision-tree inductio...
As a classifier, a Set Enumeration (SE) tree can be viewed as a generalization of decision trees. We...
Part 2: AlgorithmsInternational audienceDecision trees are among the most popular classification alg...
This paper studies the effects on decision tree learning of constructing four types of attribute (co...
Abstract. In the paper, a new method for cost-sensitive learning of decision trees is proposed. Our ...
We discuss an approach to constructing composite features during the induction of decision trees. Th...
Abstract. Decision tree learning represents a well known family of inductive learning algo-rithms th...