Variable Threshold based Feature Selection using Spatial Distribution of Data.
10.4258/jksmi.2009.15.4.475
- Author:
Chang Sik SON
1
;
A Mi SHIN
;
Young Dong LEE
;
Hee Joon PARK
;
Hyoung Seob PARK
;
Yoon Nyun KIM
Author Information
1. Biomedical Informatics Technology Center, Keimyung University, Korea.
- Publication Type:Original Article
- Keywords:
Feature Selection;
Variable Threshold;
Pattern Classification;
Dyspnea Patients
- MeSH:
Data Mining;
Dyspnea
- From:Journal of Korean Society of Medical Informatics
2009;15(4):475-481
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
OBJECTIVE: In processing high dimensional clinical data, choosing the optimal subset of features is important, not only for reduce the computational complexity but also to improve the value of the model constructed from the given data. This study proposes an efficient feature selection method with a variable threshold. METHODS: In the proposed method, the spatial distribution of labeled data, which has non-redundant attribute values in the overlapping regions, was used to evaluate the degree of intra-class separation, and the weighted average of the redundant attribute values were used to select the cut-off value of each feature. RESULTS: The effectiveness of the proposed method was demonstrated by comparing the experimental results for the dyspnea patients' dataset with 11 features selected from 55 features by clinical experts with those obtained using seven other classification methods. CONCLUSION: The proposed method can work well for clinical data mining and pattern classification applications.
