In order to improve the generalization ability of binary decision trees, a new learning algorithm, the MMDT algorithm, is presented. Based on statistical learning theory the generalization performance of binary decisi...In order to improve the generalization ability of binary decision trees, a new learning algorithm, the MMDT algorithm, is presented. Based on statistical learning theory the generalization performance of binary decision trees is analyzed, and the assessment rule is proposed. Under the direction of the assessment rule, the MMDT algorithm is implemented. The algorithm maps training examples from an original space to a high dimension feature space, and constructs a decision tree in it. In the feature space, a new decision node splitting criterion, the max-min rule, is used, and the margin of each decision node is maximized using a support vector machine, to improve the generalization performance. Experimental results show that the new learning algorithm is much superior to others such as C4. 5 and OCI.展开更多
In order to meet the strict requirements for information in engineering management, the positive interval （0, 1 ] in Shannon information entropy is extended to the real number interval [ - 1, 1 ]. The information the...In order to meet the strict requirements for information in engineering management, the positive interval （0, 1 ] in Shannon information entropy is extended to the real number interval [ - 1, 1 ]. The information theory and the decision theory are combined effectively, and the deficiencies that the traditional Bayes decision-making methods only consider a single factor are made up for. The multi-factors engineering decision-making methods are proposed, and some critical problems are solved in the practical engineering management decision-making process.展开更多
文摘In order to improve the generalization ability of binary decision trees, a new learning algorithm, the MMDT algorithm, is presented. Based on statistical learning theory the generalization performance of binary decision trees is analyzed, and the assessment rule is proposed. Under the direction of the assessment rule, the MMDT algorithm is implemented. The algorithm maps training examples from an original space to a high dimension feature space, and constructs a decision tree in it. In the feature space, a new decision node splitting criterion, the max-min rule, is used, and the margin of each decision node is maximized using a support vector machine, to improve the generalization performance. Experimental results show that the new learning algorithm is much superior to others such as C4. 5 and OCI.
文摘In order to meet the strict requirements for information in engineering management, the positive interval （0, 1 ] in Shannon information entropy is extended to the real number interval [ - 1, 1 ]. The information theory and the decision theory are combined effectively, and the deficiencies that the traditional Bayes decision-making methods only consider a single factor are made up for. The multi-factors engineering decision-making methods are proposed, and some critical problems are solved in the practical engineering management decision-making process.
