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Apirat Wanichsombat. Feature selection based on hierarchical concept model using formal concept analysis. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2021.
Feature selection based on hierarchical concept model using formal concept analysis
Name: Patsita Wetchapram
Organization :
Prince of Songkla University, Surat Thani Campus. Fauclty of science and Industrial Technology
Email :
6240320503@psu.ac.th
Name: Jirapond Muangprathub
Organization :
Prince of Songkla University, Surat Thani Campus. Fauclty of science and Industrial Technology
Email :
jirapond.m@psu.ac.th
Name: Boonyarit Choopradit
Organization :
Thammasat University. Fauclty of science and Technology
Email :
boonyarit@mathstat.sci.tu.ac.th
Name: Apirat Wanichsombat
Organization :
Prince of Songkla University, Surat Thani Campus. Fauclty of science and Industrial Technology
Email :
apirat.w@psu.ac.th
keyword:
Feature selection (Machine learning)
LCSH:
Machine learning
-- Classification
LCSH:
Data mining
LCSH:
Decision trees
Abstract:
Feature selection is used to reduce the number of input variables when using a predictive model. The hierarchical concept model is one approach for feature selection that exploits dependency relationships among hierarchically structured features. Thus, this work proposes feature selection based on a hierarchical concept model using formal concept analysis. The high levels that include a set of structural attributes are selected because these levels indicate general knowledge that represents some of the properties in child nodes. We experiment with selecting the top three high levels of hierarchical structure. Likewise, we compare the proposed model with the decision tree approach based on the hierarchical concept model using the top three high levels. The classification task is used to test the proposed model using 10 datasets from the UCI machine learning repository. The results show that both hierarchical concept models are not significantly different in classification performance.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2021
Modified:
2026-04-20
Issued:
2026-04-20
บทความ/Article
application/pdf
BibliograpyCitation :
In Chiang Mai University. Faculty of Engineering. 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2021) (pp.299-302). Chiang Mai : Chiang Mai University
eng
©copyrights King Mongkut's University of Technology North Bangkok
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ใช้เวลา
-0.971177 วินาที
Patsita Wetchapram
| Title | Contributor | Type |
|---|---|---|
| Feature selection based on hierarchical concept model using formal concept analysis
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Patsita Wetchapram;Jirapond Muangprathub;Boonyarit Choopradit;Apirat Wanichsombat | บทความ/Article |
Jirapond Muangprathub
| Title | Contributor | Type |
|---|---|---|
| Forecasting tourist arrivals with keyword search using time series
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Patthamaphon Kaewmanee;Jirapond Muangprathub;Wichuta Sae-Jie | บทความ/Article | |
| Feature selection based on hierarchical concept model using formal concept analysis
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Patsita Wetchapram;Jirapond Muangprathub;Boonyarit Choopradit;Apirat Wanichsombat | บทความ/Article |
Boonyarit Choopradit
| Title | Contributor | Type |
|---|---|---|
| Feature selection based on hierarchical concept model using formal concept analysis
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Patsita Wetchapram;Jirapond Muangprathub;Boonyarit Choopradit;Apirat Wanichsombat | บทความ/Article |
Apirat Wanichsombat
| Title | Contributor | Type |
|---|