摘要
Attribute reduction is an important issue of data mining. It is generally regarded as a preprocessing phase that alleviates the curse of dimensionality, though it also leads to classificatory analysis of decision tables. In this paper, we propose an efficient algorithm TWI-SQUEEZE that can find a minimal (or irreducible) attribute subset, which preserves classificatory consistency after two scans of a decision table. Its worst-case computational complexity is analyzed. The outputs of the algorithm are two different kinds of classifiers. One is an IF-THEN rule system. The other is a decision tree.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | Intelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings |
| 出版商 | Springer Verlag |
| 页 | 859-868 |
| 页数 | 10 |
| ISBN（印刷版） | 3540454853, 9783540454854 |
| DOI | |
| 出版状态 | 已出版 - 2006 |
| 活动 | 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 - Burgos, 西班牙 期限: 20 9月 2006 → 23 9月 2006 |
出版系列
| 姓名 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| 卷 | 4224 LNCS |
| ISSN（印刷版） | 0302-9743 |
| ISSN（电子版） | 1611-3349 |
会议
| 会议 | 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 |
|---|---|
| 国家/地区 | 西班牙 |
| 市 | Burgos |
| 时期 | 20/09/06 → 23/09/06 |
指纹
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He, Y. (2006). An efficient attribute reduction algorithm. 在 Intelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings (页码 859-868). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 4224 LNCS). Springer Verlag. https://doi.org/10.1007/11875581_103