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| 論文名稱 | New C-fuzzy decision tree with classified points |
| 發表日期 | 2008-12-01 |
| 論文收錄分類 | SCI |
| 所有作者 | Shiueng Bien Yang |
| 作者順序 | 第一作者 |
| 通訊作者 | 否 |
| 刊物名稱 | Journal of Electronic Imaging |
| 發表卷數 | 17 |
| 是否具有審稿制度 | 是 |
| 發表期數 | 4 |
| 期刊或學報出版地國別/地區 | NATTWN-中華民國 |
| 發表年份 | 2008 |
| 發表月份 | 1 |
| 發表形式 | 電子期刊 |
| 所屬計劃案 | 無 |
| 可公開文檔 |
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| 可公開文檔 |
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| 可公開文檔 | |
| 附件 |
 New C-fuzzy decision tree with classified points.pdf
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[英文摘要] :
The C-fuzzy decision tree (CFDT)–based on the fuzzy
C-means (FCM) algorithm has been proposed recently. In many experiments,
the CFDT performs better than the “standard” decision
tree, namely, the C4.5. A new C-fuzzy decision tree (NCFDT) is
proposed, and it outperforms the CFDT. Two design issues for
NCFDT are as follows. First, the growing method of NCFDT is
based on both classification error rate and the average number of
comparisons for the decision tree, whereas that of CFDT only addresses
classification error rate. Thus, the proposed NCFDT performs
better than the CFDT. Next, the classified point replaces the
cluster center to classify the input vector in the NCFDT. The
classified-points searching algorithm is proposed to search for one
classified point in each cluster. The classification error rate of the
NCFDT with classified points is smaller than that of CFDT with cluster
centers. Furthermore, these classified points can be applied to
the CFDT to reduce classification error rate. The performance of
NCFDT is compared to CFDT and other methods in experiments.
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