AbstractThis paper presents a new tool for study of relationships between total path length (average depth) and number of terminal nodes for decision trees. These relationships are important from the point of view of optimization of decision trees. In this particular case of total path length and number of terminal nodes, the relationships between these two cost functions are closely related with space-time trade-off. In addition to algorithm to compute the relationships, the paper also presents results of experiments with datasets from UCI ML Repository1. These experiments show how two cost functions behave for a given decision table and the resulting plots show the Pareto frontier or Pareto set of optimal points. Furthermore, in some case...
We define the decision problem DATA ARRANGEMENT, which involves arranging the vertices of a graph G ...
AbstractIn this paper, we present three approaches for construction of decision rules for decision t...
In this paper, based on the results of rough set theory, test theory, and exact learning, we investi...
AbstractThis paper presents a new tool for study of relationships between total path length (average...
Abstract—We used decision tree as a model to discover the knowledge from multi-label decision tables...
AbstractIn this paper, we present the empirical results for relationships between time (depth) and s...
A decision tree accepts or rejects input strings x1,..., xn and can be described as a directed tree ...
The traditional problem in binary decision diagrams (BDDs) has been to minimize the number of nodes ...
We describe five heuristic techniques to optimize decision trees of uniform depth, that is, to minim...
<p>Comparison of decision tree dimensions on 40 UCI datasets including the number of leaves.</p
In this paper, we address the issue of evaluating decision trees generated from training examples by...
Machine learning algorithms are used to learn models capable of predicting on unseen data. In recent...
Construction algorithms of optimum and near-optimum decision trees are surveyed under two optimality...
An elaborated digital computer programme supporting the time-consuming process of selecting the impo...
Inferring a decision tree from a given dataset is a classic problem in machine learning. This proble...
We define the decision problem DATA ARRANGEMENT, which involves arranging the vertices of a graph G ...
AbstractIn this paper, we present three approaches for construction of decision rules for decision t...
In this paper, based on the results of rough set theory, test theory, and exact learning, we investi...
AbstractThis paper presents a new tool for study of relationships between total path length (average...
Abstract—We used decision tree as a model to discover the knowledge from multi-label decision tables...
AbstractIn this paper, we present the empirical results for relationships between time (depth) and s...
A decision tree accepts or rejects input strings x1,..., xn and can be described as a directed tree ...
The traditional problem in binary decision diagrams (BDDs) has been to minimize the number of nodes ...
We describe five heuristic techniques to optimize decision trees of uniform depth, that is, to minim...
<p>Comparison of decision tree dimensions on 40 UCI datasets including the number of leaves.</p
In this paper, we address the issue of evaluating decision trees generated from training examples by...
Machine learning algorithms are used to learn models capable of predicting on unseen data. In recent...
Construction algorithms of optimum and near-optimum decision trees are surveyed under two optimality...
An elaborated digital computer programme supporting the time-consuming process of selecting the impo...
Inferring a decision tree from a given dataset is a classic problem in machine learning. This proble...
We define the decision problem DATA ARRANGEMENT, which involves arranging the vertices of a graph G ...
AbstractIn this paper, we present three approaches for construction of decision rules for decision t...
In this paper, based on the results of rough set theory, test theory, and exact learning, we investi...