Results 311 to 320 of about 9,752,550 (347)
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Approximating XGBoost with an interpretable decision tree
Information Sciences, 2021Omer Sagi, L. Rokach
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Journal of the ACM, 1990
The reduction algorithm is a technique for improving a decision tree in the abseence of aproecise cost criterion. The result of applying the algorithm is an irreducible tree that is no less efficient than the original, and may be more efficient. Irreducible trees arise in discrete decision theory as an algebraic form for decision trees.
J. Robin B. Cockett, J. A. Hierrera
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The reduction algorithm is a technique for improving a decision tree in the abseence of aproecise cost criterion. The result of applying the algorithm is an irreducible tree that is no less efficient than the original, and may be more efficient. Irreducible trees arise in discrete decision theory as an algebraic form for decision trees.
J. Robin B. Cockett, J. A. Hierrera
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Efficient and Secure Decision Tree Classification for Cloud-Assisted Online Diagnosis Services
IEEE Transactions on Dependable and Secure Computing, 2019Decision tree classification has become a prevailing technique for online diagnosis services. By outsourcing computation intensive tasks to a cloud server, cloud-assisted online diagnosis services are better ways for cases that the storage and ...
Jinwen Liang +4 more
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Decision Trees for Decision Tables
1994We investigate decision trees for decision tables. We present some upper and lower bounds on the minimal decision tree depth. These bounds are expressed by some parameters of decision rule systems constructed for decision tables.
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Speech emotion recognition based on DNN-decision tree SVM model
Speech Communication, 2019Motivated by the development of DNN technology, a speech emotion recognition method based on DNN-decision tree SVM model is proposed. The proposed method can not only excavate the deep emotion information of the speech signal, but also extract more ...
Linhui Sun +4 more
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Intelligent Data Analysis, 2000
The article presents the extension of a common decision tree concept to a multidimensional -- vector -- decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample.
Matej Sprogar +4 more
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The article presents the extension of a common decision tree concept to a multidimensional -- vector -- decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample.
Matej Sprogar +4 more
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Knowledge and Information Systems, 2008
Ranking problems have recently become an important research topic in the joint field of machine learning and information retrieval. This paper presented a new splitting rule that introduces a metric, i.e., an impurity measure, to construct decision trees for ranking tasks.
Fen Xia +3 more
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Ranking problems have recently become an important research topic in the joint field of machine learning and information retrieval. This paper presented a new splitting rule that introduces a metric, i.e., an impurity measure, to construct decision trees for ranking tasks.
Fen Xia +3 more
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On decision trees for orthants
Information Processing Letters, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On Optimization of Decision Trees
2005In the paper algorithms are considered which allow to consecutively optimize decision trees for decision tables with many-valued decisions relatively different complexity measures such as number of nodes, weighted depth, average weighted depth, etc. For decision tables over an arbitrary infinite restricted information system [5] these algorithms have ...
Igor Chikalov +2 more
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Decision trees with AND, OR queries
Proceedings of Structure in Complexity Theory. Tenth Annual IEEE Conference, 2002We investigate decision trees in which one is allowed to query threshold functions of subsets of variables. We are mainly interested in the case where only queries of AND and OR are allowed. This model is a generalization of the classical decision tree model.
Yosi Ben-Asher, Ilan Newman
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