The construction of optimal decision trees for the problem stated within can be accomplished by an exhaustive enumeration. This paper discusses two approaches. The section on heuristic methods gives mostly negative results (E.G. there is no merit factor that will always yield the optimal tests, etc.), but most to these methods do give good results. The section entitled "Exhaustive Enumeration Revisited" indicates some powerful shortcuts that can be applied to an exhaustive enumeration, extending the range of this method
This thesis investigates the problem of growing decision trees from data, for the purposes of classi...
All currently known algorithms for learning decision trees are based on the paradigm of heuristic to...
Decision trees are often desirable for classification/regression tasks thanks to their human-friendl...
Decision trees are often used for decision support since they are fast to train, easy to understand ...
Decision trees are often used for decision support since they are fast to train, easy to understand ...
Construction algorithms of optimum and near-optimum decision trees are surveyed under two optimality...
The search space for the feature selection problem in decision tree learning is the lattice of subse...
Decision trees are one of the main methods for solving decision problems. The goal of this thesis is...
In this paper, we address the issue of evaluating decision trees generated from training examples by...
Abstract. Decision tree learning represents a well known family of inductive learning algo-rithms th...
Many systems have been developed for constructing decision trees from collections of examples. Alt...
Abstract. Decision tree induction techniques attempt to find small trees that fit a training set of ...
This paper treats the problem of conversion of decision tables to decision trees. In most cases, the...
Decision tree learning is a widely used approach in machine learning, favoured in applications that ...
We describe five heuristic techniques to optimize decision trees of uniform depth, that is, to minim...
This thesis investigates the problem of growing decision trees from data, for the purposes of classi...
All currently known algorithms for learning decision trees are based on the paradigm of heuristic to...
Decision trees are often desirable for classification/regression tasks thanks to their human-friendl...
Decision trees are often used for decision support since they are fast to train, easy to understand ...
Decision trees are often used for decision support since they are fast to train, easy to understand ...
Construction algorithms of optimum and near-optimum decision trees are surveyed under two optimality...
The search space for the feature selection problem in decision tree learning is the lattice of subse...
Decision trees are one of the main methods for solving decision problems. The goal of this thesis is...
In this paper, we address the issue of evaluating decision trees generated from training examples by...
Abstract. Decision tree learning represents a well known family of inductive learning algo-rithms th...
Many systems have been developed for constructing decision trees from collections of examples. Alt...
Abstract. Decision tree induction techniques attempt to find small trees that fit a training set of ...
This paper treats the problem of conversion of decision tables to decision trees. In most cases, the...
Decision tree learning is a widely used approach in machine learning, favoured in applications that ...
We describe five heuristic techniques to optimize decision trees of uniform depth, that is, to minim...
This thesis investigates the problem of growing decision trees from data, for the purposes of classi...
All currently known algorithms for learning decision trees are based on the paradigm of heuristic to...
Decision trees are often desirable for classification/regression tasks thanks to their human-friendl...