Improving the Therapeutic Performance of a Medical Bayesian Network Using Noisy Threshold Models

2005 ◽  
pp. 161-172 ◽  
Author(s):  
Stefan Visscher ◽  
Peter Lucas ◽  
Marc Bonten ◽  
Karin Schurink
Keyword(s):  
Bayesian Network ◽  
Threshold Models

Denoising of Images using wavelet Bayesian Network

2017 ◽  
Author(s):  
Prof. Anil Bavaskar ◽  
Sangita Kulkarni
Keyword(s):  
Bayesian Network

Bayesian network ensemble learning with inadequate information

2014 ◽  
Author(s):  
Ruijie Du ◽  
Shuangcheng Wang ◽  
Cuiping Leng ◽  
Yunbin Fu
Keyword(s):  
Bayesian Network ◽  
Ensemble Learning ◽  
Inadequate Information

A Resamping Approach for Customer Gender Prediction Based on E-Commerce Data

2017 ◽  
Vol 3 (1) ◽  
pp. 76 ◽  
Author(s):  
Duong Tran Duc ◽  
Pham Bao Son ◽  
Tan Hanh ◽  
Le Truong Thien
Keyword(s):  
Data Mining ◽  
Bayesian Network ◽  
Web Applications ◽  
Service Providers ◽  
Main Idea ◽  
Classification Methods ◽  
Cost Sensitive Learning ◽  
Network Method ◽  
Demographic Attributes ◽  
Privacy Issues

Demographic attributes of customers such as gender, age, etc. provide the important information for e-commerce service providers in marketing, personalization of web applications. However, the online customers often do not provide this kind of information due to the privacy issues and other reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems, such as the date and time of access, the products viewed, etc. The main idea is that we extract the features from catalog viewing information and employ the classification methods to predict the gender of the viewers. The experiments were conducted on the datasets provided by the PAKDD’15 Data Mining Competition and obtained the promising results with a simple feature design, especially with the Bayesian Network method along with other supporting techniques such as resampling, cost-sensitive learning, boosting etc.

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