We describe five heuristic techniques to optimize decision trees of uniform depth, that is, to minimize their storage requirement. We also compare them in terms of speed and effectiveness. © 1991.link_to_subscribed_fulltex
\u3cp\u3eWe encode the problem of learning the optimal decision tree of a given depth as an integer ...
Decision trees are often desirable for classification/regression tasks thanks to their human-friendl...
Existing algorithms for learning optimal decision trees can be put into two categories: algorithms b...
Abstract — Decision tree algorithms are among the most popular techniques for dealing with classific...
Construction algorithms of optimum and near-optimum decision trees are surveyed under two optimality...
In this paper, we address the issue of evaluating decision trees generated from training examples by...
Decision tree learning is a widely used approach in machine learning, favoured in applications that ...
All currently known algorithms for learning decision trees are based on the paradigm of heuristic to...
The construction of optimal decision trees for the problem stated within can be accomplished by an e...
Abstract—We used decision tree as a model to discover the knowledge from multi-label decision tables...
The power of certain heuristic rules is indicated by the relative reduction in the complexity of com...
AbstractIn this paper, we present the empirical results for relationships between time (depth) and s...
This paper presents the problem of finding parame-ter settings of algorithms for building decision t...
We encode the problem of learning the optimal decision tree of a given depth as an integer optimizat...
The interest in algorithms for learning optimal decision trees (ODTs) has increased significantly in...
\u3cp\u3eWe encode the problem of learning the optimal decision tree of a given depth as an integer ...
Decision trees are often desirable for classification/regression tasks thanks to their human-friendl...
Existing algorithms for learning optimal decision trees can be put into two categories: algorithms b...
Abstract — Decision tree algorithms are among the most popular techniques for dealing with classific...
Construction algorithms of optimum and near-optimum decision trees are surveyed under two optimality...
In this paper, we address the issue of evaluating decision trees generated from training examples by...
Decision tree learning is a widely used approach in machine learning, favoured in applications that ...
All currently known algorithms for learning decision trees are based on the paradigm of heuristic to...
The construction of optimal decision trees for the problem stated within can be accomplished by an e...
Abstract—We used decision tree as a model to discover the knowledge from multi-label decision tables...
The power of certain heuristic rules is indicated by the relative reduction in the complexity of com...
AbstractIn this paper, we present the empirical results for relationships between time (depth) and s...
This paper presents the problem of finding parame-ter settings of algorithms for building decision t...
We encode the problem of learning the optimal decision tree of a given depth as an integer optimizat...
The interest in algorithms for learning optimal decision trees (ODTs) has increased significantly in...
\u3cp\u3eWe encode the problem of learning the optimal decision tree of a given depth as an integer ...
Decision trees are often desirable for classification/regression tasks thanks to their human-friendl...
Existing algorithms for learning optimal decision trees can be put into two categories: algorithms b...