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In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both...
As a complementary and alternative system to Western medicine, traditional Chinese medicine (TCM) forms an integrated and unique approach to treat diseases. In response to the subjectivity and fuzziness of TCM, quantitative methods are needed. In TCM, the symptoms are often high dimensional and the redundant and irrelevant symptoms may degrade the performance of classifiers. Therefore, a critical...
In this paper, for the refinement of the database in data mining, by synthetically analyzing the characteristics of the current attribute reduction methods and decision tree algorithm, we put forward formalized description model of rule knowledge, and establish a kind of attribute reduction method (BD-RED) of decision tree by using similarity between rules families. Further, we discuss the construction...
We describe an approach to learning predictive models from large databases in settings where direct access to data is not available because of massive size of data, access restrictions, or bandwidth requirements. We outline some techniques for minimizing the number of statistical queries needed; and for efficiently coping with missing values in the data. We provide open source implementation of the...
Boosting is the most popular method of improving quality and stabilizing weak classifiers. It bases on the voting by the group of classifiers, where each of them is generated on the basis of modified original learning set. The modification of AdaBoost.M1 and experimental results of boosted C4.5 (decision tree induction) algorithm are presented. All experimental researches are made on well known benchmark...
MBBNTree algorithm, which integrates the advantage of Markov blanket Bayesian networks (MBBN) and decision tree, would behave better performance than other Bayesian networks for classification. But the available training samples with actual classes are not enough for building MBBNTree classifier in practice. Active learning aims at reducing the number of training examples to be labeled by automatically...
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