Hierarchical Classification and Regression with Feature Selection

Shih Wen Ke, Chi Wei Yeh

研究成果: 書貢獻/報告類型會議論文篇章同行評審

1 引文 斯高帕斯(Scopus)

摘要

Previous studies proposed different hierarchical estimation approaches for solving certain specific domain problems. They usually combine two or more estimation models in a hierarchical fashion. Therefore, in our previous work [2], we proposed a hierarchical approach for generic purposes, the Hierarchical Classification and Regression (HCR), that incorporates classification and estimation techniques. The HCR [2] approach significantly outperformed three benchmark flat estimation models. Having seen the potential of the proposed HCR as a generic hierarchical regression scheme, we propose to further improve the HCR by introducing feature selection (FS) techniques to the HCR. In order to thoroughly investigate the effect of FS on the HCR, we examine different numbers of attributes remained after feature selection with respect to datasets of various sizes. The results showed that the HCR with linear regression performed significantly better than the other HCRs while feature selection helped lower the RMSE slightly with only 50% of the original features.

原文???core.languages.en_GB???
主出版物標題2019 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2019
發行者IEEE Computer Society
頁面1150-1154
頁數5
ISBN(電子)9781728138046
DOIs
出版狀態已出版 - 12月 2019
事件2019 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2019 - Macao, Macao
持續時間: 15 12月 201918 12月 2019

出版系列

名字IEEE International Conference on Industrial Engineering and Engineering Management
ISSN(列印)2157-3611
ISSN(電子)2157-362X

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???2019 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2019
國家/地區Macao
城市Macao
期間15/12/1918/12/19

指紋

深入研究「Hierarchical Classification and Regression with Feature Selection」主題。共同形成了獨特的指紋。

引用此