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Learning decision tree for ranking
Knowledge and Information Systems, 2008Decision tree is one of the most effective and widely used methods for classification. However, many real-world applications require instances to be ranked by the probability of class membership. The area under the receiver operating characteristics curve, simply AUC, has been recently used as a measure for ranking performance of learning algorithms ...
Liangxiao Jiang, Chaoqun Li, Zhihua Cai
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Transfer Learning in Decision Trees
2007 International Joint Conference on Neural Networks, 2007Most research in machine learning focuses on scenarios in which a learner faces a single learning task, independently of other learning tasks or prior knowledge. In reality, however, learning is not performed in isolation, starting from scratch with every new task. Instead, it is a lifelong activity during which a learner encounters many learning tasks,
Jun won Lee, Christophe Giraud-Carrier
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Relational Query Synthesis ⋈ Decision Tree Learning
Proceedings of the VLDB Endowment, 2023We study the problem of synthesizing a core fragment of relational queries called select-project-join (SPJ) queries from input-output examples. Search-based synthesis techniques are suited to synthesizing projections and joins by navigating the network of relational tables but require additional supervision for synthesizing comparison predicates.
Aaditya Naik +4 more
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Decision tree-based paraconsistent learning
Proceedings. SCCC'99 XIX International Conference of the Chilean Computer Science Society, 2003It is possible to apply machine learning, uncertainty management and paraconsistent logic concepts to the design of a paraconsistent learning system, able to extract useful knowledge even in the presence of inconsistent information in a database. This paper presents a decision tree-based machine learning technique capable of handling inconsistent ...
F. Enembreck, B.C. Avila, R. Sabourin
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2016
The main objective of this chapter is to introduce you to hierarchical supervised learning models. One of the main hierarchical models is the decision tree. It has two categories: classification tree and regression tree. The theory and applications of these decision trees are explained in this chapter.
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The main objective of this chapter is to introduce you to hierarchical supervised learning models. One of the main hierarchical models is the decision tree. It has two categories: classification tree and regression tree. The theory and applications of these decision trees are explained in this chapter.
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Learning Decision Trees Using the Fourier Spectrum
SIAM Journal on Computing, 1991Summary: This work gives a polynomial time algorithm for learning decision trees with respect to the uniform distribution. (This algorithm uses membership queries.) The decision tree model that is considered is an extension of the traditional Boolean decision tree model that allows linear operations in each node (i.e., summation of a subset of the ...
Kushilevitz, Eyal, Mansour, Yishay
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Competitive learning in decision trees
AIP Conference Proceedings, 1998In this paper, a competitive learning rule is introduced in decision trees as a computationally attractive scheme for adaptive density estimation or lossy compression. It is shown by simulation that the adaptive decision tree performs at least as well as other competitive learning algorithms while being much faster.
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Decision Trees Learning System
2002This paper describes computer system — Decision Trees Learning System (DTLS) that was developed as a main part of the Master Thesis: “Implementation of the algorithms of learning decision trees, working on any SQL compatible database”. Developed System is friendly, credible environment for searching and modeling knowledge stored in databases, using ...
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Learning decision trees using confusion entropy
2013 International Conference on Machine Learning and Cybernetics, 2013Confusion entropy is a new measure for evaluating performance of classifiers. For each class in a classification problem, the CEN metric considers not only the misclassification information about how the true samples in this class have been misclassified to the other classes, but also the misclassification information about how the other samples have ...
null Han Jin +4 more
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