Problem reduction methods and a tree generation algorithm for the steiner network problem

Networks ◽  
1987 ◽  
Vol 17 (1) ◽  
pp. 65-85 ◽  
Author(s):  
Anantaram Balakrishnan ◽  
Nitin R. Patel
Keyword(s):  
Generation Algorithm ◽  
Network Problem ◽  
Tree Generation ◽  
Problem Reduction ◽  
Reduction Methods

An Improved Test Tree Generation Algorithm from a Graphical Model

2021 ◽  
Author(s):  
Pan Liu ◽  
Yihao Li ◽  
Hao Chen ◽  
Xuankui Zheng ◽  
Sihao Huang
Keyword(s):  
Graphical Model ◽  
Generation Algorithm ◽  
Tree Generation ◽  
Test Tree

scholarly journals A novel approach to dynamic profiling of e-customers considering click stream data and online reviews

2019 ◽  
Vol 9 (1) ◽  
pp. 602
Author(s):  
Houda Zaim ◽  
Adil Haddi ◽  
Mohammed Ramdani
Keyword(s):  
Online Reviews ◽  
Satisfaction Level ◽  
Stream Data ◽  
Online Service ◽  
Generation Algorithm ◽  
Preference Information ◽  
Fuzzy Variables ◽  
Novel Approach ◽  
Tree Generation ◽  
Over Time

<p>In this paper, we present an approach for mining change in customer’s behavior for the purpose of maintaining robust profiling model over time. Most of previous studies leave important questions unanswered: In developing B2C e-commerce strategies, how do managers implicitly load customer’s profiles based on their satisfaction over the online store characteristics? And: What kind of feedback segments do they have? Our proposed approach does not force customers to explicitly express their preference information over the online service but rather capture their preference from their online activities. The challenge does not only lay in analyzing how customer’s classifier model change and when it does so but also to adapt it to the customer’s click stream data using a new decision tree generation algorithm which takes as inputs new set of variables; categorical, continuous and fuzzy variables. Customer’s online reviews rates are considered as classes. Experiments show that this work performed well in identifying relevant customer’s stream data to judge the chinese e-commerce website “Tmall”. The extracted values of the website’s features are also useful to identifying the satisfaction level when the customer’s rate is not available.</p><p> </p>

An Efficient Tree-Generation Algorithm

1981 ◽  
Vol 27 (3) ◽  
pp. 105-109 ◽  
Author(s):  
A. Rakshit ◽  
S. Sen Sarma ◽  
R. K. Sen ◽  
A.K. Choudhury
Keyword(s):  
Generation Algorithm ◽  
Tree Generation

Decision Tree Generation Algorithm without Pruning

2013 ◽  
Vol 441 ◽  
pp. 731-737
Author(s):  
Jing Gao
Keyword(s):  
Decision Tree ◽  
Rough Set ◽  
Classification Accuracy ◽  
Negative Impact ◽  
Decision Tree Algorithm ◽  
Generation Algorithm ◽  
Tree Algorithm ◽  
Tree Generation ◽  
Negative Effect

On the generation of decision tree based on rough set model, for the sake of classification accuracy, existing algorithms usually partition examples too specific. And it is hard to avoid the negative impact caused by few special examples on decision tree. In order to obtain this priority in traditional decision tree algorithm based on rough set, the sample is partitioned much more meticulously. Inevitably, a few exceptional samples have negative effect on decision tree. And this leads that the generated decision tree seems too large to be understood. It also reduces the ability in classifying and predicting the coming data. To settle these problems, the restrained factor is introduced in this paper. For expanding nodes in generating decision tree algorithm, besides traditional terminating condition, an additional terminating condition is involved when the restrained factor of sample is higher than a given threshold, then the node will not be expanded any more. Thus, the problem of much more meticulous partition is avoided. Furthermore, the size of decision tree generated with restrained factor involved will not seem too large to be understood.

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