Traditionally, the algorithm of id3 takes the information gain as a standard of expanding attributes. During the process of selection of expanded attributes, attributes with more values are usually preferred to be sel...
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ISBN:
(纸本)9781424458219;9781424458240
Traditionally, the algorithm of id3 takes the information gain as a standard of expanding attributes. During the process of selection of expanded attributes, attributes with more values are usually preferred to be selected. To solve such problem, a kind of AED algorithm based on average Euclidean distance in decision tree is proposed in this paper. The algorithm uses the average Euclidean distance as heuristic information. The experiment results show that the improved AED algorithm can avoid the variety bias of id3 algorithm, and has no worse classification precision and less time cost than id3.
When noise exists in case base, high quality knowledge is hard to obtain by id3 *** the weakness, by introducing the concept of second learning, the noisy data can be removed, which not only develop the decision tree,...
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When noise exists in case base, high quality knowledge is hard to obtain by id3 *** the weakness, by introducing the concept of second learning, the noisy data can be removed, which not only develop the decision tree, but also it can make good structure tree *** that we can abstract good rules information, and make the desirable tret more ***, the more the data can be mined by decision tree algorithm, the better the efficiency and performance of the algorithm is, and the more obvious the superiority of algorithm *** paper states the basic idea of algorithm, implementation process, performance analysis and accuracy proof in detail.
Decision tree algorithm is not only the important part of machine learning, but also the most widely used data mining tool. At present, there are many algorithms of generating decision tree, but when the database whic...
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Decision tree algorithm is not only the important part of machine learning, but also the most widely used data mining tool. At present, there are many algorithms of generating decision tree, but when the database which we rely on exists noise, high quality knowledge is hard to obtain by 103algorithm. In this paper, we propose the data mining method based on second learning in case of id3 algorithm, and analyze the performance of our method by a concrete database. Theory analysis and simulation indicate that this method posses the feature of strong operability, and it can improve the reliability of obtained knowledge.
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