Decision Tree Pruning Using by Cai Jingfeng (5 results)

Taschenbuch. Condition: Neu. Decision Tree Pruning Using Expert Knowledge | Cost-sensitive Pruning | Jingfeng Cai | Taschenbuch | 236 S. | Englisch | 2013 | VDM Verlag Dr. Müller e.K. | EAN 9783836491556 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Decision tree technology has proven to be a valuable way of capturing human decision making within a computer. How to prune the decision tree is one of the research directions of the decision tree technique, but the idea of cost-sensitive pruning has received much less attention than other pruning techniques even though additional flexibility and increased performance can be obtained from this method. This dissertation reports on a study of cost-sensitive methods for decision tree pruning. A decision tree pruning algorithm called KBP1.0, which includes four cost-sensitive methods, is developed. The intelligent inexact classification is used for first time in KBP1.0 to prune the decision tree. Using expert knowledge in decision tree pruning is discussed for the first time. By comparing the cost-sensitive pruning methods in KBP1.0 with other traditional pruning methods on benchmark data sets, the advantage and disadvantage of cost-sensitive methods in KBP1.0 have been summarized. This research will enhance our understanding of the theory, design and implementation of decision tree pruning using expert knowledge. 236 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Decision tree technology has proven to be a valuable way of capturing human decision making within a computer. How to prune the decision tree is one of the research directions of the decision tree technique, but the idea of cost-sensitive pruning has receiv.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Decision tree technology has proven to be a valuable way of capturing human decision making within a computer. How to prune the decision tree is one of the research directions of the decision tree technique, but the idea of cost-sensitive pruning has received much less attention than other pruning techniques even though additional flexibility and increased performance can be obtained from this method. This dissertation reports on a study of cost-sensitive methods for decision tree pruning. A decision tree pruning algorithm called KBP1.0, which includes four cost-sensitive methods, is developed. The intelligent inexact classification is used for first time in KBP1.0 to prune the decision tree. Using expert knowledge in decision tree pruning is discussed for the first time. By comparing the cost-sensitive pruning methods in KBP1.0 with other traditional pruning methods on benchmark data sets, the advantage and disadvantage of cost-sensitive methods in KBP1.0 have been summarized. This research will enhance our understanding of the theory, design and implementation of decision tree pruning using expert knowledge.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 236 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Decision tree technology has proven to be a valuable way of capturing human decision making within a computer. How to prune the decision tree is one of the research directions of the decision tree technique, but the idea of cost-sensitive pruning has received much less attention than other pruning techniques even though additional flexibility and increased performance can be obtained from this method. This dissertation reports on a study of cost-sensitive methods for decision tree pruning. A decision tree pruning algorithm called KBP1.0, which includes four cost-sensitive methods, is developed. The intelligent inexact classification is used for first time in KBP1.0 to prune the decision tree. Using expert knowledge in decision tree pruning is discussed for the first time. By comparing the cost-sensitive pruning methods in KBP1.0 with other traditional pruning methods on benchmark data sets, the advantage and disadvantage of cost-sensitive methods in KBP1.0 have been summarized. This research will enhance our understanding of the theory, design and implementation of decision tree pruning using expert knowledge.