Stable L2-Regularized Ensemble Feature Weighting

2013 ◽  
pp. 167-178 ◽  
Author(s):  
Yun Li ◽  
Shasha Huang ◽  
Songcan Chen ◽  
Jennie Si
Keyword(s):  
Feature Weighting

scholarly journals MOOC Dropout Prediction Using FWTS-CNN Model Based on Fused Feature Weighting and Time Series

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Ya-Feng Zheng ◽  
Zhang-Hao Gao ◽  
Yi-Hang Wang ◽  
Qian Fu
Keyword(s):  
Time Series ◽  
Feature Weighting ◽  
Model Based

Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces

2012 ◽  
Vol 8 (2) ◽  
pp. 44-63 ◽  
Author(s):  
Baoxun Xu ◽  
Joshua Zhexue Huang ◽  
Graham Williams ◽  
Qiang Wang ◽  
Yunming Ye
Keyword(s):  
Random Forest ◽  
High Dimensional Data ◽  
Real Life ◽  
Classification Performance ◽  
Feature Weighting ◽  
Random Forest Model ◽  
High Dimensional ◽  
Forest Model ◽  
Forest Models ◽  
Random Forest Models

The selection of feature subspaces for growing decision trees is a key step in building random forest models. However, the common approach using randomly sampling a few features in the subspace is not suitable for high dimensional data consisting of thousands of features, because such data often contains many features which are uninformative to classification, and the random sampling often doesn’t include informative features in the selected subspaces. Consequently, classification performance of the random forest model is significantly affected. In this paper, the authors propose an improved random forest method which uses a novel feature weighting method for subspace selection and therefore enhances classification performance over high-dimensional data. A series of experiments on 9 real life high dimensional datasets demonstrated that using a subspace size of features where M is the total number of features in the dataset, our random forest model significantly outperforms existing random forest models.

Two feature weighting approaches for naive Bayes text classifiers

2016 ◽  
Vol 100 ◽  
pp. 137-144 ◽  
Author(s):  
Lungan Zhang ◽  
Liangxiao Jiang ◽  
Chaoqun Li ◽  
Ganggang Kong
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Feature Weighting ◽  
Text Classifiers

A proposal for evolutionary fuzzy systems using feature weighting: Dealing with overlapping in imbalanced datasets

2015 ◽  
Vol 73 ◽  
pp. 1-17 ◽  
Author(s):  
Saleh Alshomrani ◽  
Abdullah Bawakid ◽  
Seong-O Shim ◽  
Alberto Fernández ◽  
Francisco Herrera
Keyword(s):  
Fuzzy Systems ◽  
Feature Weighting ◽  
Imbalanced Datasets ◽  
Evolutionary Fuzzy Systems
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