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Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.


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Proceedings ArticleDOI
08 Oct 2012
TL;DR: A novel soft-output detection algorithm for superposition modulation using a sequential tree search approach with Gaussian approximation on the unknown layers and the most significant symbols can be identified and used to approximate the marginal posterior probabilities.
Abstract: We present a novel soft-output detection algorithm for superposition modulation. Using a sequential tree search approach with Gaussian approximation (TS-GA) on the unknown layers, the most significant symbols can be identified and used to approximate the marginal posterior probabilities. The detector has low and fixed computational complexity. Simulation results demonstrate that the optimal a posteriori probability (APP) performance can be approached with a small number of symbol candidates.

3 citations

Proceedings ArticleDOI
01 Aug 2008
TL;DR: The necessity of revisable data association for SLAM is analyzed and graph search of AI is used to model and solve the revising data association problem.
Abstract: This paper illustrates the reason why revisable data association is needed for the simultaneous localization and mapping (SLAM) of mobile robots, and an incremental SLAM algorithm with backtrack searching data association is presented. Our approach uses a tree model called correspondence tree (CT) to represent the solution space of the data association problem. CT is layered according to time steps and every node in it is a data association hypothesis for the measurements gotten at-a-time. A best-first with limit backtracking search strategy is designed to find the optimal path in CT. A state estimation method based on the least-squares problem is developed. This method can compute the cost of nodes in CT and update state estimation incrementally, so direct feedback is introduced from the state estimation process to the data association model. With the interaction between data association and state estimation, and combining with tree pruning techniques, our approach can get accurate data association and state estimation for online SLAM applications. The contribution of this paper is that we have analyzed the necessity of revisable data association for SLAM and we use graph search of AI to model and solve the revising data association problem.

3 citations

Book ChapterDOI
01 Jan 2010
TL;DR: In this article, a decision tree approach was used to determine the key variables to be used for classification and ultimately extract salinized soil from other cover and soil types within the study area.
Abstract: Extracting information about saline soils from remote sensing data can be useful, particularly given the environmental significance and changing nature of these soils in arid environments. One interesting case study is the delta oasis of the Ugan and Kuqa rivers in China’s Xinjiang region, which was studied using a landsat enhanced thematic mapper plus (ETM+) image collected in August 2001. In recent years, decision tree classifiers have been used successfully for land cover classification from remote sensing data. Principal component analysis (PCA) is a popular data reduction technique used to help build a decision tree; it reduces complexity and can help improve the classification precision of a decision tree. A decision tree approach was used to determine the key variables to be used for classification and ultimately extract salinized soil from other cover and soil types within the study area. The third principal component (PC3) is an effective variable in the decision tree classification for salinized soil information extraction. The PC3 was the best band to identify areas of severely salinized soil; the blue spectral band from the ETM+ sensor (TM1) was the best band to identify salinized soil with the salt-tolerant vegetation of tamarisk (Tamarix chinensis Lour); and areas comprising mixed water bodies and vegetation can be identified using the spectral indices Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI). Based upon this analysis, a decision tree classifier was applied to classify land cover types with different levels of soil saline. The overall accuracy of the classification was 94.80%, which suggests that the decision tree model was a simple and effective method with relatively high precision.

3 citations

Book ChapterDOI
22 Feb 2018
TL;DR: Intelligent Decision Framework (IDF) to explore and manage cases of hepatitis c virus based on data mining approach and Fuzzy logic system and improves the predication results of Fibrosis stage by using Trapezoidal FuzzY Number distribution as fuzzy logical system.
Abstract: This research presents Intelligent Decision Framework (IDF) to explore and manage cases of hepatitis c virus based on data mining approach and Fuzzy logic system. The proposed framework is produced from integration between data mining decision tree, rule based classification and fuzzy logic system. On the other hand, this study improves the predication results of Fibrosis stage by using Trapezoidal Fuzzy Number (TFN) distribution as fuzzy logical system to arrive 98.1% compared to predication results that were 92.5% by data mining decision tree model for same patients sample. Fuzzy logic system predicts disease scale of Hepatitis C Virus (HCV) for patients sample through different stages of liver disease caused by virus c. The proposed framework supports physicians and Ministry of Health (MOH) strategies for treatment to limit and control HCV infections and prevalence rate in Egypt and other countries. The extracted knowledge and information from proposed framework helps decision makers to take appropriate and better decision at appropriate time to against hepatitis c viral in world. The architecture of intelligent decision framework is designed to support physicians to investigate and present treatment for HCV cases. Also, to develop intelligent machine, health care system or robots as a physician for HCV patients in high prevalence rate countries.

3 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202310
202224
2021101
2020163
2019158
2018121