scholarly journals Feature grouping and selection: A graph-based approach

2021 ◽  
Vol 546 ◽  
pp. 1256-1272
Author(s):  
Ling Zheng ◽  
Fei Chao ◽  
Neil Mac Parthaláin ◽  
Defu Zhang ◽  
Qiang Shen
Keyword(s):  
Feature Grouping

scholarly journals Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping

2021 ◽  
Vol 11 (11) ◽  
pp. 4742
Author(s):  
Tianpei Xu ◽  
Ying Ma ◽  
Kangchul Kim
Keyword(s):  
Ensemble Learning ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Prediction System ◽  
Churn Prediction ◽  
Customer Churn ◽  
Feature Grouping ◽  
Recognition Systems ◽  
The Cost ◽  
Better Than

In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.

Scatter search for high-dimensional feature selection using feature grouping

2021 ◽  
Author(s):  
Miguel García-Torres ◽  
Francisco Gómez-Vela ◽  
Federico Divina ◽  
Diego P. Pinto-Roa ◽  
José Luis Vázquez Noguera ◽  
...  
Keyword(s):  
Feature Selection ◽  
Scatter Search ◽  
High Dimensional ◽  
Feature Grouping

Feature grouping-based parallel outlier mining of categorical data using spark

2019 ◽  
Vol 504 ◽  
pp. 1-19
Author(s):  
Junli Li ◽  
Jifu Zhang ◽  
Xiao Qin ◽  
Yaling Xun
Keyword(s):  
Categorical Data ◽  
Outlier Mining ◽  
Feature Grouping

Making Trillion Correlations Feasible in Feature Grouping and Selection

2016 ◽  
Vol 38 (12) ◽  
pp. 2472-2486 ◽  
Author(s):  
Yiteng Zhai ◽  
Yew-Soon Ong ◽  
Ivor W. Tsang
Keyword(s):  
Feature Grouping

Feature Grouping and Selection on High-Dimensional Microarray Data

2015 ◽  
Author(s):  
Miguel Garcia-Torres ◽  
Francisco Gomez-Vela ◽  
David Becerra-Alonso ◽  
Belen Melian-Batista ◽  
J. Marcos Moreno-Vega
Keyword(s):  
Microarray Data ◽  
High Dimensional ◽  
Feature Grouping
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