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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
Yugo Murawaki1
01 Nov 2020
TL;DR: A probabilistic generative model that represents latent factors as geographical distributions is proposed that has higher affinity with the tree model because a tree can alternatively be represented as a set of geographical distributions.
Abstract: Analyzing the evolution of dialects remains a challenging problem because contact phenomena hinder the application of the standard tree model. Previous statistical approaches to this problem resort to admixture analysis, where each dialect is seen as a mixture of latent ancestral populations. However, such ancestral populations are hardly interpretable in the context of the tree model. In this paper, we propose a probabilistic generative model that represents latent factors as geographical distributions. We argue that the proposed model has higher affinity with the tree model because a tree can alternatively be represented as a set of geographical distributions. Experiments involving synthetic and real data suggest that the proposed method is both quantitatively and qualitatively superior to the admixture model.

5 citations

Proceedings ArticleDOI
24 Sep 2012
TL;DR: It is shown that the use of improved ID3 algorithm to deal with the customer base data samples can reduce the computational cost, and improve the efficiency of the decision tree generation.
Abstract: Customer information-mining in e-commerce is very important. ID3 algorithm is a mining one based on decision tree, which selects property value with the highest gains as the test attribute of its sample sets, establishes decision-making node, and divides them in turn. ID3 algorithm involves repeated logarithm operation, and it will affect the efficiency of generating decision tree when there are a large number of data, so one must change the selection criteria of data set attributes, using the Taylor formula to transform the algorithm to reduce the amount of data calculation and the generation time of decision trees and thus improve the efficiency of the decision tree classifier. It is shown that the use of improved ID3 algorithm to deal with the customer base data samples can reduce the computational cost, and improve the efficiency of the decision tree generation.

5 citations

Journal Article
TL;DR: In this paper, a decision tree based classification method is used to extract and classify the urban wetland information in Shanghai area using multispectral bands of Landsat-5 TM image as the main variables, and a series of derivative data as the auxiliary inputs, derived from the Landsat 5 TM images by using respectively K-T transformation, IHS transformation, principal component analysis and textural analysis.
Abstract: Urban wetland is an important ecological basis of Shanghai and it is characterized with complex properties.In this study,a decision tree based classification method is used to extract and classify the urban wetland information in Shanghai area.The method uses multispectral bands of Landsat-5 TM image as the main variables,and a series of derivative data as the auxiliary inputs,derived from the Landsat-5 TM images by using respectively K-T transformation,IHS transformation,principal component analysis and textural analysis.With these variables in association with the spatial characteristics of the urban wetland in Shanghai,the method builds a decision tree model for urban wetland extraction and classification.The application of the model shows that the total area of the urban wetland in Shanghai is about 1 277.40 km2.The rice cultivated area occupies the highest portion up to 65.30% of the total wetland,and the next the area of rivers,ponds,lakes and reed fields.The decision tree model based method has a relative high precision in the urban wetland extraction and classification.The classification result indicates that the overall accuracy reaches 89.05%,more than 10% increase when compared with the maximum likelihood algorithm.

5 citations

Proceedings ArticleDOI
XianMin Wei1
11 Nov 2010
TL;DR: The improved algorithm to construct a decision tree by using statistical theory and ideas of conditional probability is proposed in this paper, and experiments show that the computational complexity of this decision tree algorithm is superior to the traditional algorithm, and its efficiency is greatly improved.
Abstract: Decision tree algorithm is a very active research area of data mining. This paper describes the basic decision tree idea in data mining, then discusses the computational complexity of the classical decision tree algorithm (ID3 algorithm). And the improved algorithm to construct a decision tree by using statistical theory and ideas of conditional probability is proposed in this paper. Experiments show that the computational complexity of this decision tree algorithm is superior to the traditional algorithm, and its efficiency is greatly improved.

5 citations

Proceedings ArticleDOI
16 May 2012
TL;DR: A new decision tree algorithm that uses multiple attributes to construct a core vector generated from two farthest records and recursively partition the dataset along this core vector using the vector projection.
Abstract: Network intrusion problem has been received more attention during the past few years due to the increase company network usages. Many network intrusion systems have been proposed and in cooperated various classifiers to identify malicious packages among all regular network packages using the past history. Decision tree algorithm is one of the popular adapted classifier. It utilizes the training records to build a decision tree model which select the best split of a single attribute among all candidate attributes that best classifies training records. To facilitate a combination of attributes, the decision tree must apply a finite number of branches which may generate a tall tree. Attributes may relate in a more complex setting that they need to be simultaneously used for branching. This paper proposes a new decision tree algorithm that uses multiple attributes to construct a core vector generated from two farthest records. Then the algorithm recursively partition the dataset along this core vector using the vector projection. The best split is identified along this core vector based on the information gain. Our results show the improvement of the network intrusion problem from UCI over the regular decision tree algorithm.

5 citations


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