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In the inductive learning, if example bank has noise, it is hard to obtain decision trees with high precision, that is, it is hard to obtain knowledge with high credibility. So, this paper puts forward C-ID3 algorithm, by which we compute confidence level of example bank firstly, and further get a kind of decision tree on the basis of traditional ID3 and the credibility obtained. All the theory analysis...
When noise exists in case base, high quality knowledge is hard to obtain by ID3 algorithm. For 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 generate. So that we can abstract good rules information, and make the desirable tree more accurate. Especially, the more the data...
Induce learning is one of the most important research areas on application of artificial intelligence. The classification algorithm is the core of the machine learning. The article advanced a kind of decision classification algorithm that is in view of attribute value statistical regularity, on the characteristic and insufficient of ID3, C4.5 algorithm. (In short of BS-CA) This algorithm takes the...
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