Sparse kernel logistic regression based on L 1/2 regularization

2012 ◽  
Vol 56 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Chen Xu ◽  
ZhiMing Peng ◽  
WenFeng Jing
Keyword(s):  
Logistic Regression ◽  
Kernel Logistic Regression ◽  
Sparse Kernel

The evidence framework applied to sparse kernel logistic regression

2005 ◽  
Vol 64 ◽  
pp. 119-135 ◽  
Author(s):  
Gavin C. Cawley ◽  
Nicola L.C. Talbot
Keyword(s):  
Logistic Regression ◽  
Kernel Logistic Regression ◽  
Evidence Framework ◽  
Sparse Kernel

Sparse kernel logistic regression for β-turns prediction

2012 ◽  
Author(s):  
Murtada Khalafallah Elbashir ◽  
Jianxin Wang ◽  
Fang-Xiang Wu ◽  
Min Li
Keyword(s):  
Logistic Regression ◽  
Kernel Logistic Regression ◽  
Sparse Kernel

Predicting Mobile Portability Across Telecommunication Networks Using the Integrated-KLR

2021 ◽  
Vol 17 (3) ◽  
pp. 50-62
Author(s):  
Ayodeji Samuel Makinde ◽  
Abayomi O. Agbeyangi ◽  
Wilson Nwankwo
Keyword(s):  
Logistic Regression ◽  
Telecommunication Networks ◽  
Mobile Service ◽  
Personal Choice ◽  
Kernel Logistic Regression ◽  
Service Efficiency ◽  
Number Portability ◽  
Prediction Techniques ◽  
The Cost ◽  
Potential Customers

Mobile number portability (MNP) across telecommunication networks entails the movement of a customer from one mobile service provider to another. This, often, is as a result of seeking better service delivery or personal choice. Churning prediction techniques seek to predict customers tending to churn and allow for improved customer sustenance campaigns and the cost therein through an improved service efficiency to customer. In this paper, MNP predicting model using integrated kernel logistic regression (integrated-KLR) is proposed. The Integrated-KLR is a combination of kernel logistic regression and expectation-maximization clustering which helps in proactively detecting potential customers before defection. The proposed approach was evaluated with five others, mostly used algorithms: SOM, MLP, Naïve Bayes, RF, J48. The proposed iKLR outperforms the other algorithms with ROC and PRC of 0.856 and 0.650, respectively.

scholarly journals Learning kernel logistic regression in the presence of class label noise

2014 ◽  
Vol 47 (11) ◽  
pp. 3641-3655 ◽  
Author(s):  
Jakramate Bootkrajang ◽  
Ata Kabán
Keyword(s):  
Logistic Regression ◽  
Class Label ◽  
Label Noise ◽  
Kernel Logistic Regression
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