scholarly journals Evaluating causal‐based feature selection for fuel property prediction models

2021 ◽  
Author(s):  
Bernard Nguyen ◽  
Leanne S. Whitmore ◽  
Anthe George ◽  
Corey M. Hudson
Keyword(s):  
Feature Selection ◽  
Prediction Models ◽  
Property Prediction ◽  
Fuel Property ◽  
Selection For

A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results

2016 ◽  
Vol 71 ◽  
pp. 76-85 ◽  
Author(s):  
Farideh Bagherzadeh-Khiabani ◽  
Azra Ramezankhani ◽  
Fereidoun Azizi ◽  
Farzad Hadaegh ◽  
Ewout W. Steyerberg ◽  
...  
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Variable Selection ◽  
Prediction Models ◽  
Selection Methods ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Selection For

FEATURE SELECTION FOR DATASETS WITH IMBALANCED CLASS DISTRIBUTIONS

2010 ◽  
Vol 20 (02) ◽  
pp. 113-137 ◽  
Author(s):  
ABU H. M. KAMAL ◽  
XINGQUAN ZHU ◽  
ABHIJIT PANDYA ◽  
SAM HSU ◽  
RAMASWAMY NARAYANAN
Keyword(s):  
Feature Selection ◽  
Credit Card ◽  
Prediction Models ◽  
Accurate Prediction ◽  
Minority Class ◽  
Sample Distribution ◽  
Data Collection Process ◽  
Selection For ◽  
Class Labels ◽  
Data Collections

Feature selection for supervised learning concerns the problem of selecting a number of important features (w.r.t. the class labels) for the purposes of training accurate prediction models. Traditional feature selection methods, however, fail to take the sample distributions into consideration which may lead to poor prediction for minority class examples. Due to the sophistication and the cost involved in the data collection process, many applications, such as biomedical research, commonly face biased data collections with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). For these applications, the minority class examples, such as disease samples, credit card frauds, and network intrusions, are only a small portion of the data but deserve full attention for accurate prediction. In this paper, we propose three filtering techniques, Higher Weight (HW), Differential Minority Repeat (DMR) and Balanced Minority Repeat (BMR), to identify important features from datasets with biased sample distribution. Experimental comparisons with the ReliefF method on five datasets demonstrate the effectiveness of the proposed methods in selecting informative features for accurate prediction of minority class examples.

Chicken Algorithm Based Feature Selection For Arabic Information Searching System

2019 ◽  
pp. 389
Author(s):  
زينب عبدالأمير ◽  
علياء كريم عبدالحسن
Keyword(s):  
Feature Selection ◽  
Information Searching ◽  
Selection For
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