scholarly journals Wrapper Feature Subset Selection for Dimension Reduction Based on Ensemble Learning Algorithm

2015 ◽  
Vol 72 ◽  
pp. 162-169 ◽  
Author(s):  
Rattanawadee Panthong ◽  
Anongnart Srivihok
Keyword(s):  
Dimension Reduction ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Ensemble Learning Algorithm ◽  
Selection For

scholarly journals An Ensemble Co-Evolutionary based Algorithm for Classification Problems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Vũ Văn Trường ◽  
Bùi Thu Lâm ◽  
Nguyễn Thành Trung
Keyword(s):  
Ensemble Learning ◽  
Learning Algorithm ◽  
Feature Subset Selection ◽  
Learning Approach ◽  
Feature Subset ◽  
Classification Problems ◽  
Classification Result ◽  
Classifier Design ◽  
Optimal Classifier ◽  
Ensemble Learning Algorithm

In this paper, the authors propose a dual-population co-evolutionary approach using ensemble learning approach (E-SOCA)  to  simultaneously  solve  both  feature  subset selection  and  optimal  classifier  design.  Different  from previous  studies  where  each  population  retains  only  one best individual (Elite) after co-evolution, in this study, an elite  community  will  be  stored  and  calculated  together through  an  ensemble  learning  algorithm  to  produce  the final    classification    result.    Experimental    results    on standard  UCI  problems  with  a  variety  of  input  features ranging from small to large sizes shows that the proposed algorithm  results  in  more  accuracy  and  stability  than traditional algorithms.

scholarly journals Klasifikasi Resiko Kehamilan Menggunakan Ensemble Learning berbasis Classification Tree

2021 ◽  
Vol 6 (3) ◽  
pp. 177
Author(s):  
Muhamad Arief Hidayat
Keyword(s):  
Ensemble Learning ◽  
Classification Tree ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Pregnancy Risk ◽  
Risk Status ◽  
Cost Sensitive Learning ◽  
Best Value ◽  
Selection Stage

In health science there is a technique to determine the level of risk of pregnancy, namely the Poedji Rochyati score technique. In this evaluation technique, the level of pregnancy risk is calculated from the values ​​of 22 parameters obtained from pregnant women. Under certain conditions, some parameter values ​​are unknown. This causes the level of risk of pregnancy can not be calculated. For that we need a way to predict pregnancy risk status in cases of incomplete attribute values. There are several studies that try to overcome this problem. The research "classification of pregnancy risk using cost sensitive learning" [3] applies cost sensitive learning to the process of classifying the level of pregnancy risk. In this study, the best classification accuracy achieved was 73% and the best value was 77.9%. To increase the accuracy and recall of predicting pregnancy risk status, in this study several improvements were proposed. 1) Using ensemble learning based on classification tree 2) using the SVMattributeEvaluator evaluator to optimize the feature subset selection stage. In the trials conducted using the classification tree-based ensemble learning method and the SVMattributeEvaluator at the feature subset selection stage, the best value for accuracy was up to 76% and the best value for recall was up to 89.5%

A PSO-Based Feature Subset Selection for Application of Spam /Non-spam Detection

2013 ◽  
pp. 183-193 ◽  
Author(s):  
Amir Rajabi Behjat ◽  
Aida Mustapha ◽  
Hossein Nezamabadi-pour ◽  
Md. Nasir Sulaiman ◽  
Norwati Mustapha
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
Spam Detection ◽  
Feature Subset ◽  
Selection For

Feature subset selection for cancer detection using various rank-based algorithms

2021 ◽  
Vol 13 (4) ◽  
pp. 346
Author(s):  
N. Arulmurugaselvi ◽  
M. Praneetha ◽  
B. Surendiran ◽  
E. Sri Hari Keerthi ◽  
D. Swetha ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Selection For

scholarly journals Feature subset selection for improved native accent identification

2010 ◽  
Vol 52 (2) ◽  
pp. 83-98 ◽  
Author(s):  
Tingyao Wu ◽  
Jacques Duchateau ◽  
Jean-Pierre Martens ◽  
Dirk Van Compernolle
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Selection For ◽  
Accent Identification
Sign in / Sign up

Export Citation Format

Share Document