Multivariate Decision Tree Algorithm Based on Rough Set
Year: 2012
Type: article
Abstract: The decision tree is a widely used classification model and inductive learning method based on examples. It is characterized by the simple classification rules, easy understanding for users and so on, but we also can see some disadvantages in certain situations. The paper puts forward the multivariable decision tree algorithm which based on a rough set to a combination of rough sets theory and decision tree algorithm. The multivariable decision tree algorithm has reduced the complexity of decision tree while not affect the readability of the classification rules. Experimental analysis has witnessed the feasibility and efficiency of the algorithm.
Source: Applied Mechanics and Materials
Authors: Bing Xiang Liu, Yan Wu, Meng Shan Li
Institution: Jingdezhen Ceramic Institute
Language: English
Topic: Rough Sets and Fuzzy Logic
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W1994180261