Developing a Machine Learning Model for Hydrogen Bond Acceptance Based on Natural Bond Orbital Descriptors. [PDF]
J Org ChemMelo DU +4 more
europepmc +1 more source
Generalized coarsened confounding for causal effects: a large-sample framework. [PDF]
J Causal InferenceGhosh D, Wang L.
europepmc +1 more source
On the Entropy-Based Localization of Inequality in Probability Distributions. [PDF]
Entropy (Basel)Rajaram R, Ritchey N, Castellani B.
europepmc +1 more source
Interpretable Ensemble Architectures with Theory-Informed Features for High-Fidelity Real-Time Congestion Forecasting on the Chalong Rat Expressway. [PDF]
Sensors (Basel)Puttima P, Zhou T, Chen Z.
europepmc +1 more source
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)openaire
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
openaire +2 more sources
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
openaire +2 more sources
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.
null Desheng Yin +2 more
openaire +2 more sources
The Based on Rough Set Theory Development of Decision Tree after Redundant Dimensional Reduction
2015 Fifth International Conference on Advanced Computing & Communication Technologies, 2015 Decision tree technologists have been examined to be a helpful way to find out the human decision making within a host. Decision tree performs variable screening or feature selection. It requires relatively lesser effort from the users for the preparation of the data.
Priya Pal, Deepak Motwani
openaire +2 more sources
A Framework for a Decision Tree Learning Algorithm with Rough Set Theory
, 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
Masaki Kurematsu +2 more
+4 more sources