Topic
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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Journal Article•
TL;DR: Traditional decision tree and weighted decision tree algorithms are compared results from both training and testing dataset for heart disease.
Abstract: In this paper, we proposed the traditional decision tree algorithm and weighted decision tree algorithm. Traditional decision tree algorithm consists of C4.5 and CART algorithms. The weighted decision tree algorithm is to set appropriate weights of training instances based on naive Bayesian theorem before trying to construct a decision tree model. We compare the proposed weighted decision tree algorithm with traditional C4.5 and CART algorithms. In this paper, traditional decision tree and weighted decision tree algorithms are compared results from both training and testing dataset for heart disease.
01 Jan 2001
TL;DR: The model is inspired by the theory of Fourier optics and it is proved the model can simulate analog recurrent neural networks, thus establishing a lower bound on its computational power.
Abstract: We prove computability and complexity results for an original model of computation. Our model is inspired by the theory of Fourier optics. We prove our model can simulate analog recurrent neural networks, thus establishing a lower bound on its computational power. We also prove some computational complexity results for searching and sorting algorithms expressed with our model.
Patent•
14 Jul 2017
TL;DR: In this paper, the authors proposed a typical resource element extraction method and apparatus for land cover classification of remote sensing data, which consists of selecting sample data of a plurality of land surface characteristic parameter products at the same temporal position and land classification data at the temporal position corresponding to the sample data.
Abstract: The present invention relates to a typical resource element extraction method and apparatus The method comprises: selecting sample data of a plurality of land surface characteristic parameter products at the same temporal position and land classification data at the temporal position corresponding to the sample data; associating the sample data with the land classification data, and converting the sample data and the land classification data into relational data; training a decision tree model according to the relational data; and according to the trained decision tree model, carrying out typical resource element extraction on the to-be-classified land classification data According to the typical resource element extraction method and apparatus provided by the present invention, by selecting the sample data of the plurality of land surface characteristic parameter products at the same temporal position and the land classification data at the temporal position corresponding to the sample data, and by carrying out typical resource element extraction on the to-be-classified land classification data according to the trained decision tree model, the accuracy of land cover classification of remote sensing data can be significantly improved, and the rationality and credibility of the classification algorithm can be improved
TL;DR: This study proposes a modeling method of new growth rules based on the convolution sums of divisor functions based on an existing growth-volume based algorithm for efficient management of the branches and leaves that constitute a tree, as well as natural propagation of branches.
Abstract: In order to model a variety of natural trees that are appropriate to outdoor terrains consisting of multiple trees, this study proposes a modeling method of new growth rules(based on the convolution sums of divisor functions). Basically, this method uses an existing growth-volume based algorithm for efficient management of the branches and leaves that constitute a tree, as well as natural propagation of branches. The main features of this paper is to introduce the theory of convolution sums of divisor functions that is naturally expressed the growth or fate of branches and leaves at each growth step. Based on this, a method of modeling various tree is proposed to minimize user control through a number of divisor functions having generalized generation functions and modification of the growth rule. This modeling method is characterized by its consideration of both branches and leaves as well as its advantage of having a greater effect on the construction of an outdoor terrain composed of multiple trees. Natural and varied tree model creation through the proposed method was conducted, and using this, the possibility of constructing a wide nature terrain and the efficiency of the process for configuring multiple trees were evaluated experimentally.
01 Jun 2016
TL;DR: A complexity scalable mode decision algorithm is proposed for complexity control of HEVC through statistics to get the optimal mode combination for each target complexity.
Abstract: Due to limited computational capability in handheld devices, complexity constrained video coding draws great attention in recent years. In this paper, a complexity scalable mode decision algorithm is proposed for complexity control of HEVC. First, complexity is properly mapped to a target in terms of prediction modes. Then a mode decision algorithm is proposed through statistics to get the optimal mode combination for each target complexity. Experimental results show that the proposed algorithm achieves a very large complexity control range of 10%–100% while maintaining good Rate-Distortion performance. For lowdelayP condition, an average gain of 1.1 dB in BDPSNR is obtained for 22 sequences when the complexity is around 20%.