Results 251 to 260 of about 10,905,813 (301)

CAMUS: scalable phylogenetic network estimation. [PDF]

Bioinformatics
Willson J, Warnow T.
europepmc   +1 more source

Stand-alone graph-based lung airway labeling with quantitative validations. [PDF]

Med Phys
Chen H, Modiri A, Sawant A.
europepmc   +1 more source

TS-IBD: An efficient ancestral recombination graph-based identity by descent segment detection method. [PDF]

Hum Popul Genet Genom
Wei Y, Sanaullah A, Zhi D, Zhang S.
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SOME RESULTS IN THE EXTENSION WITH A COHERENT SUSLIN TREE (Aspects of Descriptive Set Theory)

SOME RESULTS IN THE EXTENSION WITH A COHERENT SUSLIN TREE (Aspects of Descriptive Set Theory)
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A Framework for a Decision Tree Learning Algorithm with Rough Set Theory

Communications in Computer and Information Science, 2015
In this paper, we improve the conventional decision tree learning algorithm using rough set theory. First, our approach gets the upper approximate for each class. Next, it generates the decision tree from each upper approximate. Each decision tree shows whether the data item is in this class or not. Our approach classifies the unlabeled data item using
Hamido Fujita, Jun Hakura
exaly   +2 more sources

An Integration of Cloud Transform and Rough Set Theory to Induction of Decision Trees

Fundamenta Informaticae, 2009
Decision trees are one of the most popular data-mining techniques for knowledge discovery. Many approaches for induction of decision trees often deal with the continuous data and missing values in information systems. However, they do not perform well in real situations.
Jing Song, Tianrui Li 0001, Da Ruan 0001
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Construction of Decision Tree Based on Rough Sets Theory

Advanced Materials Research, 2012
In the process of constructing decision trees, the selecting criteria of classification attributes will directly affect the classification results. Here we presented the classification contribution function (CCF), a new concept based on rough sets theory, which is regarded as the criteria for choosing attributes in the core of attributes.
Zhi Jun Lei, Lin Li Wu
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An Algorithm for Decision Tree Construction Based on Rough Set Theory

2008 International Conference on Computer Science and Information Technology, 2008
In this paper, a novel and effective algorithm is introdcued for constructing decision tree. First of all, the knowledge dependence in rough set theory is used to reduce the test attribute set of decision tree, that is, the test attribute space is optimized and hence the attributes which are not correlated with the decision information are deleted ...
Cuiru Wang, Fangfang Ou
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A New Decision Tree Algorithm Based on Rough Set Theory

2009 Asia-Pacific Conference on Information Processing, 2009
Decision tree algorithm has been widely used to classify numeric and categorical attributes. Lots of approaches were suggested in order to induce decision trees. ID3 (Quinlan, 1986), as a heuristic algorithm, is very classic and popular in the induction of decision trees.
Baoshi Ding, Yongqing Zheng, Shaoyu Zang
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Data-driven decision tree learning algorithm based on rough set theory

Proceedings of the 2005 International Conference on Active Media Technology, 2005. (AMT 2005)., 2005
Decision tree pre-pruning is an effective method to solve the over-fitting problem in decision tree learning process. However, it is difficult to estimate the exact time to stop the growing process of a decision tree, which limits the developments and applications of this method.
Desheng Yin, Guoyin Wang 0001, Yu Wu 0001   +2 more
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