A Hybrid Efficient Feature Selection Model for High Dimensional Data Set based on KNHNAES (2013~2015)

• Published in: 디지털콘텐츠학회논문지 Vol. 19; no. 4; pp. 739 - 747
• Main Authors: Tae il Kwon(권태일), Dingkun Li(이정곤), Hyun Woo Park(박현우), Kwang Sun Ryu(류광선), Eui Tak Kim(김의탁), Minghao Piao(박명호)
• Format: Journal Article
• Language: English
• Published: 한국디지털콘텐츠학회 01.04.2018
• Subjects: 컴퓨터학; 병렬 컴퓨팅; hybrid feature selection model; high dimensional data; data mining; 고차원 데이터; 하이브리드 특징 선택 모델; parallel computing; 데이터 마이닝; feature selection; 특징 선택
• ISSN: 1598-2009; 2287-738X
• DOI: 10.9728/dcs.2018.19.4.739

With a large feature space data, feature selection has become an extremely important procedure in the Data Mining process. But the traditional feature selection methods with single process may no longer fit for this procedure. In this paper, we proposed a hybrid efficient feature selection model for high dimensional data. We have applied our model on KNHNAES data set, the result shows that our model outperforms many existing methods in terms of accuracy over than at least 5%. KCI Citation Count: 0