Add to ORKGBased on our earlier results in decision theory, we demonstrate how decision trees can be integrated into a general framework for analysing decision situations with respect to different criteria, and suggest an evaluation rule taking into account all strategies, criteria, probabilities and utilities involved in the situations under consideration. A significant property of the framework is that it admits the representation of imprecise information at all stages. This information is modelled in sets of measures constrained by interval estimates. The strategies are then evaluated relative to different decision rules, e.g., a set of generalisations of the principle of admissibility. Decision situations are evaluated using fast algorithms develo...
Bayesian decision tree analysis has been widely used as a basis for quality control decision making....
The inductive learning methodology known as decision trees, concerns the ability to classify objects...
The inductive learning methodology known as decision trees, concerns the ability to classify objects...
Abstract We present a decision tree evaluation method integrated with a common framework for analyzi...
AbstractEvaluation of decision trees in which imprecise information prevails is complicated. Especia...
Despite the fact that unguided decision making might lead to inefficient and nonoptimal decisions, d...
Despite the fact that unguided decision making might lead to inefficient and nonoptimal decisions, d...
Despite the fact that unguided decision making might lead to inefficient and nonoptimal decisions, d...
AbstractEvaluation of decision trees in which imprecise information prevails is complicated. Especia...
Decision trees are fundamental in machine learning due to their interpretability and versatility. Th...
Decision trees are one of the most powerful and commonly used supervised learning algorithms in the ...
The decision can be defined as the way chosen from several possible to achieve an objective. An imp...
The decision can be defined as the way chosen from several possible to achieve an objective. An imp...
In Chapter One of the thesis we describe the stringent information requirements and the resultant me...
The inductive learning methodology known as decision trees, concerns the ability to classify objects...
Bayesian decision tree analysis has been widely used as a basis for quality control decision making....
The inductive learning methodology known as decision trees, concerns the ability to classify objects...
The inductive learning methodology known as decision trees, concerns the ability to classify objects...
Abstract We present a decision tree evaluation method integrated with a common framework for analyzi...
AbstractEvaluation of decision trees in which imprecise information prevails is complicated. Especia...
Despite the fact that unguided decision making might lead to inefficient and nonoptimal decisions, d...
Despite the fact that unguided decision making might lead to inefficient and nonoptimal decisions, d...
Despite the fact that unguided decision making might lead to inefficient and nonoptimal decisions, d...
AbstractEvaluation of decision trees in which imprecise information prevails is complicated. Especia...
Decision trees are fundamental in machine learning due to their interpretability and versatility. Th...
Decision trees are one of the most powerful and commonly used supervised learning algorithms in the ...
The decision can be defined as the way chosen from several possible to achieve an objective. An imp...
The decision can be defined as the way chosen from several possible to achieve an objective. An imp...
In Chapter One of the thesis we describe the stringent information requirements and the resultant me...
The inductive learning methodology known as decision trees, concerns the ability to classify objects...
Bayesian decision tree analysis has been widely used as a basis for quality control decision making....
The inductive learning methodology known as decision trees, concerns the ability to classify objects...
The inductive learning methodology known as decision trees, concerns the ability to classify objects...