Results 301 to 310 of about 9,752,550 (347)

Decision tree classifier: a detailed survey

International Journal of Information and Decision Sciences, 2020
Decision tree classifier (DTC) is one of the well-known methods for data classification. The most significant feature of DTC is its ability to change the complicated decision making problems into simple processes, thus finding a solution which is ...
N. Priyanka, Dharmender Kumar
semanticscholar   +1 more source

Distributed Decision Tree

Proceedings of the 9th Annual ACM India Conference, 2016
Decision Tree is a tree-structured plan of a set of attributes to test in order to predict the output. MapReduce and Spark is a programming model used for processing data on a distributed file system. In this paper, MapReduce and Spark implementation of Decision Tree is named as Distributed Decision Tree (DDT) and Spark Tree (ST) respectively. Decision
Ankit Desai, Sanjay Chaudhary
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Decision tree-based diagnosis of coronary artery disease: CART model

Comput. Methods Programs Biomed., 2020
BACKGROUND AND OBJECTIVE As the most common cardiovascular defect, coronary artery disease (CAD), also called ischemic heart disease, is one of the substantial causes of death globally.
M. Ghiasi, S. Zendehboudi, A. Mohsenipour   +2 more
semanticscholar   +1 more source

Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers

Geocarto International, 2020
In this study, we have developed five spatially explicit ensemble predictive machine learning models for the landslide susceptibility mapping of the Van Chan district of the Yen Bai Province, Vietnam.
B. Pham, P. Tran, T. Nguyen-Thoi, Kajori Parial, Sushant K. Singh, H. Ly, Kien Nguyen, Lanh Si Ho, Hiep Van Le, Indra Prakash   +9 more
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Hybrid decision tree

Knowledge-Based Systems, 2002
In this paper, a hybrid learning approach named hybrid decision tree (HDT) is proposed. HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis. It generates the trunk of a binary HDT according to the binary information gain ratio criterion in an instance space
Zhi-Hua Zhou, Zhaoqian Chen
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Application of decision tree-based ensemble learning in the classification of breast cancer

Comput. Biol. Medicine, 2020
As a common screening and diagnostic tool, Fine Needle Aspiration Biopsy (FNAB) of the suspicious breast lumps can be used to distinguish between malignant and benign breast cytology.
M. Ghiasi, S. Zendehboudi
semanticscholar   +1 more source

Optimization of the decision tree

[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91, 2002
An approach is presented to the optimization of decision trees. A decision tree is considered optimal if it correctly classifies the known data set and has the minimal number of nodes. It is shown that it is important to decide the right order of attributes to test, for this can reduce the number of checking nodes in a decision tree. >
Won Chan Jung, J. Bush Jones, Jianhua Chen 0003   +2 more
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Decision Trees and Applications

2020
In many cases, the meaning of information is wrongly related to either the sense of data or the notion of knowledge. There is a crucial sequence of steps before information becomes knowledge and the value of data depends in the existence of information so as to produce knowledge.
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Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling.

Science of the Total Environment, 2018
The main aim of the present study is to explore and compare three state-of-the art data mining techniques, best-first decision tree, random forest, and naïve Bayes tree, for landslide susceptibility assessment in the Longhai area of China.
Wei Chen, Shuai Zhang, Renwei Li, H. Shahabi   +3 more
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Decision tree fields

2011 International Conference on Computer Vision, 2011
This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fields (CRF) which have been widely used in computer vision. In a typical CRF model the unary potentials are derived from sophisticated random forest or boosting based classifiers,
Sebastian Nowozin, Carsten Rother, Shai Bagon, Toby Sharp, Bangpeng Yao, Pushmeet Kohli   +5 more
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