Add to ORKGSuppose there is a set of objects, {A, B,...E} and a set of tests, {T1, T2,...TN). When a test is applied to an object, the result is wither T or F. Assume the test may vary in cost and the object may vary in probability or occurrence. One then hopes that an unknown object may be identified by applying a sequence if tests. The appropriate test at any point in the sequence in general should depend on the results of previous tests. The problem is to construct a good test scheme using the test cost, the probabilities of occurrence, and a table of test outcomes
In this paper, we address the issue of evaluating decision trees generated from training examples by...
When a decision table is used to find a maximum expected utility testing strategy, it is based on a ...
Machine learning algorithms are used to learn models capable of predicting on unseen data. In recent...
In several applications of automatic diagnosis and active learning a central problem is the eval- ua...
We study cost-sensitive learning of decision trees that incorporate both test costs and misclassific...
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 ...
Abstract. We study cost-sensitive learning of decision trees that incorporate both test costs and mi...
The construction of optimal decision trees for the problem stated within can be accomplished by an e...
Unmanageably bushy decision trees result when a decision analysis involves several in vestigations. ...
Abstract—It has been widely observed that there is no a single best CIT generation algorithm; instea...
This paper presents a general method for the conversion of a decision table to a sequential testing ...
In several applications of automatic diagnosis and active learning a central problem is the evaluati...
This paper addresses the problem of the explanation of the result given by a decision tree, when it ...
In this study, a linear programming model is formulated that finds an optimal strategy for many deci...
In this paper, we address the issue of evaluating decision trees generated from training examples by...
When a decision table is used to find a maximum expected utility testing strategy, it is based on a ...
Machine learning algorithms are used to learn models capable of predicting on unseen data. In recent...
In several applications of automatic diagnosis and active learning a central problem is the eval- ua...
We study cost-sensitive learning of decision trees that incorporate both test costs and misclassific...
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 ...
Abstract. We study cost-sensitive learning of decision trees that incorporate both test costs and mi...
The construction of optimal decision trees for the problem stated within can be accomplished by an e...
Unmanageably bushy decision trees result when a decision analysis involves several in vestigations. ...
Abstract—It has been widely observed that there is no a single best CIT generation algorithm; instea...
This paper presents a general method for the conversion of a decision table to a sequential testing ...
In several applications of automatic diagnosis and active learning a central problem is the evaluati...
This paper addresses the problem of the explanation of the result given by a decision tree, when it ...
In this study, a linear programming model is formulated that finds an optimal strategy for many deci...
In this paper, we address the issue of evaluating decision trees generated from training examples by...
When a decision table is used to find a maximum expected utility testing strategy, it is based on a ...
Machine learning algorithms are used to learn models capable of predicting on unseen data. In recent...