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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.


Papers
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Proceedings ArticleDOI
22 Oct 2007
TL;DR: A layered tree model of managing transportation facilities based on IPv6, which can be managed uniformly over the widely spread road network and which could support for future ITS is presented.
Abstract: Transportation facilities management faces the challenge of huge amount of geographically dispersed facilities. Communication between them is one of the critical issues. Recent studies show that IT technology is one of efficient solutions. The development of IPv6 enables the possibility of connecting all the facilities to Internet with its large address space and improved performance in routing, mobility and safety. This paper presents a layered tree model of managing transportation facilities based on IPv6. According to the proposed model, different transportation facilities can be managed uniformly over the widely spread road network. And the paper discusses how the model structure is built, the management functions and the communication features based on the model. Then, a demo application is introduced to show that the tree structure is in accordance with next generation Internet thus could support for future ITS.
Patent
03 May 2019
TL;DR: In this paper, an integrated learning regression tree with low time complexity and high accuracy is adopted, so that the problem of charging pile layout planning under the condition of data loss can be solved.
Abstract: The invention relates to an electric vehicle supply and demand difference prediction method and a charging pile layout planning method. The electric vehicle supply and demand difference prediction method comprises the steps of extracting related data from original data and constructing features; sorting the features by using the information gain rate to generate a feature sorting table; performingreverse screening on the feature sorting table to generate feature subsets with different data missing degrees; for the feature subsets with different data missing degrees, generating corresponding sub-regression tree models by utilizing an improved regression tree algorithm; integrating according to a linear regression model, and learning parameters of each sub-regression tree model; and integrating by using a voting strategy of linear regression to obtain an integrated learning regression tree model, and predicting the supply and demand difference of the vehicle. And then calculating a charging pile layout model according to the supply-demand difference of the vehicle. According to the method, the integrated learning regression tree with low time complexity and high accuracy is adopted,so that the problem of charging pile layout planning under the condition of data loss can be solved.
Posted Content
TL;DR: In this paper, the authors propose an efficient Bayes coding algorithm whose computational complexity is the polynomial order for the context tree source model, where a subsequence in each interval is generated from a different context tree model.
Abstract: The context tree source is a source model in which the occurrence probability of symbols is determined from a finite past sequence, and is a broader class of sources that includes i.i.d. and Markov sources. The proposed source model in this paper represents that a subsequence in each interval is generated from a different context tree model. The Bayes code for such sources requires weighting of the posterior probability distributions for the change patterns of the context tree source and for all possible context tree models. Therefore, the challenge is how to reduce this exponential order computational complexity. In this paper, we assume a special class of prior probability distribution of change patterns and context tree models, and propose an efficient Bayes coding algorithm whose computational complexity is the polynomial order.
Proceedings ArticleDOI
27 Aug 2021
TL;DR: In this article, the effect of overfitting displayed by a decision tree classifier model is studied and the method of resampling technique to eliminate the overfitting is implemented in the pre-pruning stage of the algorithm.
Abstract: In this paper, the effect of overfitting displayed by a decision tree classifier model is studied and the method of resampling technique to eliminate the overfitting is implemented in the pre-pruning stage of the algorithm. The classifier is built for fault classification function subjected to a synthetically generated dataset of the benchmark DAMADICS process which represents a pneumatic actuator system. The overfitting problem for both multiclass classification and binary class classification using maximum depth as the optimized hyper parameter is analyzed. The results before and after eradicating the overfit are tabulated. The performance of the model is plotted between hyper parameter chosen and the testing, training accuracy. The best fit tree model is also graphically visualized.
Patent
17 Nov 2020
TL;DR: In this paper, a machine learning-based method for detecting sensitivity of different types of tumors to radiotherapy rays based on machine learning technology is proposed. But the method is limited to the case of a single tumor type.
Abstract: The invention discloses a method for detecting sensitivity of different types of tumors to radiotherapy rays based on a machine learning technology. The method comprises a medical case database module, a case data preprocessing module, a decision tree C4.5 algorithm generation module, a decision tree pruning module, a decision tree model verification module, a patient information input module anda decision tree model calculation result output module. According to an existing case database, judgment models for sensitivity of different tumors to different rays can be established and trained, and after tumor related information of a patient is input, the detection model can automatically judge and output sensitivity results of the tumors for different rays to assist doctors in making corresponding radiotherapy plans.

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