This paper introduces a new algorithm for the induction of decision trees, based on adaptive techniques. One of the main feature of this algorithm is the application of automata theory to formalize the problem of decision tree induction and the use of a hybrid approach, which integrates both syntactical and statistical strategies. Some experimental results are also pre- sented indicating that the adaptive approach is useful in the construction of efficient learning algorithms
Among the learning algorithms, one of the most popular and easiest to understand is the decision tre...
The application of boosting procedures to decision tree algorithms has been shown to produce very ac...
The past decade has seen a significant interest on the problem of inducing decision trees that take ...
This paper presents a survey of evolutionary algorithms that are designed for decision-tree inductio...
Decision tree induction is one of the most employed methods to extract knowledge from data, since th...
This paper introduces the adaptive non-deterministic decision tree, a formal device derived from ada...
Tree automata are widely used in applications such as XML document manipulation, natural language pr...
This paper presents a survey of evolutionary algorithms that are designed for decision-tree inductio...
The ability to restructure a decision tree efficiently enables a variety of approaches to decision t...
One of the biggest problem that many data analysis techniques have to deal with nowadays is Combinat...
Part 2: AlgorithmsInternational audienceDecision trees are among the most popular classification alg...
An algorithm for learning decision trees for classification and prediction is described which conver...
Among the several tasks that evolutionary algorithms have successfully employed, the induction of c...
Abstract: This article presents an incremental algorithm for inducing decision trees equivalent to t...
Decision tree learning is an important field of machine learning. In this study we examine both form...
Among the learning algorithms, one of the most popular and easiest to understand is the decision tre...
The application of boosting procedures to decision tree algorithms has been shown to produce very ac...
The past decade has seen a significant interest on the problem of inducing decision trees that take ...
This paper presents a survey of evolutionary algorithms that are designed for decision-tree inductio...
Decision tree induction is one of the most employed methods to extract knowledge from data, since th...
This paper introduces the adaptive non-deterministic decision tree, a formal device derived from ada...
Tree automata are widely used in applications such as XML document manipulation, natural language pr...
This paper presents a survey of evolutionary algorithms that are designed for decision-tree inductio...
The ability to restructure a decision tree efficiently enables a variety of approaches to decision t...
One of the biggest problem that many data analysis techniques have to deal with nowadays is Combinat...
Part 2: AlgorithmsInternational audienceDecision trees are among the most popular classification alg...
An algorithm for learning decision trees for classification and prediction is described which conver...
Among the several tasks that evolutionary algorithms have successfully employed, the induction of c...
Abstract: This article presents an incremental algorithm for inducing decision trees equivalent to t...
Decision tree learning is an important field of machine learning. In this study we examine both form...
Among the learning algorithms, one of the most popular and easiest to understand is the decision tre...
The application of boosting procedures to decision tree algorithms has been shown to produce very ac...
The past decade has seen a significant interest on the problem of inducing decision trees that take ...