| Feature Selection via Correlation Coefficient Clustering | |
|---|---|
| 學年 | 99 |
| 學期 | 1 |
| 出版（發表）日期 | 2010-12-01 |
| 作品名稱 | Feature Selection via Correlation Coefficient Clustering |
| 作品名稱（其他語言） | |
| 著者 | Hsu, Hui-Huang; Hsieh, Cheng-Wei |
| 單位 | 淡江大學資訊工程學系 |
| 出版者 | Oulu: Academy Publisher |
| 著錄名稱、卷期、頁數 | Journal of Software 5(12), pp.1371-1377 |
| 摘要 | Feature selection is a fundamental problem in machine learning and data mining. How to choose the most problem-related features from a set of collected features is essential. In this paper, a novel method using correlation coefficient clustering in removing similar/redundant features is proposed. The collected features are grouped into clusters by measuring their correlation coefficient values. The most class-dependent feature in each cluster is retained while others in the same cluster are removed. Thus, the most class-related and mutually unrelated features are identified. The proposed method was applied to two datasets: the disordered protein dataset and the Arrhythmia (ARR) dataset. The experimental results show that the method is superior to other feature selection methods in speed and/or accuracy. Detail discussions are given in the paper. |
| 關鍵字 | Feature Selection; Clustering; Correlation Coefficient; Support Vector Machines (SVMs); Machine Learning; Classification |
| 語言 | en |
| ISSN | 1796-217X |
| 期刊性質 | 國外 |
| 收錄於 | EI |
| 產學合作 | |
| 通訊作者 | Hsu, Hui-Huang; Hsieh, Cheng-Wei |
| 審稿制度 | |
| 國別 | FIN |
| 公開徵稿 | |
| 出版型式 | 紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/58496 ) |
GoogleAnalytics