A quality index for decision tree pruning |
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| Affiliation: | 1. State Key Laboratory of Coal Resource and Safe Mining, Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China, China University of Mining and Technology, Xuzhou 22116, China;2. Hanoi University of Mining and Geology, Hanoi 10000, Viet Nam;1. School of Resources and Safety Engineering, Central South University, Changsha 410083, China;2. State Key Laboratory of Coal Resources and Safety Mining, School of Mines, China University of Mining & Technology, Xuzhou 221116, China;1. School of Mechanics and Architecture Engineering, China University of Mining & Technology, Beijing 100083, China;2. State Key Laboratory for Geomechnics & Deep Underground Engineering, Beijing 100083, China;1. Department of Civil Engineering, University of Tabriz, Tabriz, Iran;2. Facultad de Ingeniería Civil, Universidad Michoacana de San Nicolás de Hidalgo, Edificio C, Planta Baja, Ciudad Universitaria, 58040 Morelia, Mexico |
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| Abstract: | Decision tree is a divide and conquer classification method used in machine learning. Most pruning methods for decision trees minimize a classification error rate. In uncertain domains, some sub-trees that do not decrease the error rate can be relevant in pointing out some populations of specific interest or to give a representation of a large data file. A new pruning method (called DI pruning) is presented here. It takes into account the complexity of sub-trees and is able to keep sub-trees with leaves yielding to determine relevant decision rules, although they do not increase the classification efficiency. DI pruning allows to assess the quality of the data used for the knowledge discovery task. In practice, this method is implemented in the UnDeT software. |
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