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| Title | Constructing a decision tree from data with Hierarchical Class Labels |
| Creator | 唐揆 Tang, Kwei |
| Contributor | 企管系 |
| Key Words | Classification; Decision tree; Hierarchical class label |
| Date | 2009.04 |
| Date Issued | 16-Oct-2014 17:52:45 (UTC+8) |
| Summary | Most decision tree classifiers are designed to classify the data with categorical or Boolean class labels. Unfortunately, many practical classification problems concern data with class labels that are naturally organized as a hierarchical structure, such as test scores. In the hierarchy, the ranges in the upper levels are less specific but easier to predict, while the ranges in the lower levels are more specific but harder to predict. To build a decision tree from this kind of data, we must consider how to classify data so that the class label can be as specific as possible while also ensuring the highest possible accuracy of the prediction. To the best of our knowledge, no previous research has considered the induction of decision trees from data with hierarchical class labels. This paper proposes a novel classification algorithm for learning decision tree classifiers from data with hierarchical class labels. Empirical results show that the proposed method is efficient and effective in both prediction accuracy and prediction specificity. |
| Relation | Expert Systems with Applications, 36(3), 4838-4847 |
| Type | article |
| DOI | http://dx.doi.org/10.1016/j.eswa.2008.05.044 |
| dc.contributor | 企管系 | en_US |
| dc.creator (作者) | 唐揆 | zh_TW |
| dc.creator (作者) | Tang, Kwei | en_US |
| dc.date (日期) | 2009.04 | en_US |
| dc.date.accessioned | 16-Oct-2014 17:52:45 (UTC+8) | - |
| dc.date.available | 16-Oct-2014 17:52:45 (UTC+8) | - |
| dc.date.issued (上傳時間) | 16-Oct-2014 17:52:45 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/70630 | - |
| dc.description.abstract (摘要) | Most decision tree classifiers are designed to classify the data with categorical or Boolean class labels. Unfortunately, many practical classification problems concern data with class labels that are naturally organized as a hierarchical structure, such as test scores. In the hierarchy, the ranges in the upper levels are less specific but easier to predict, while the ranges in the lower levels are more specific but harder to predict. To build a decision tree from this kind of data, we must consider how to classify data so that the class label can be as specific as possible while also ensuring the highest possible accuracy of the prediction. To the best of our knowledge, no previous research has considered the induction of decision trees from data with hierarchical class labels. This paper proposes a novel classification algorithm for learning decision tree classifiers from data with hierarchical class labels. Empirical results show that the proposed method is efficient and effective in both prediction accuracy and prediction specificity. | en_US |
| dc.format.extent | 861980 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Expert Systems with Applications, 36(3), 4838-4847 | en_US |
| dc.subject (關鍵詞) | Classification; Decision tree; Hierarchical class label | en_US |
| dc.title (題名) | Constructing a decision tree from data with Hierarchical Class Labels | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1016/j.eswa.2008.05.044 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1016/j.eswa.2008.05.044 | en_US |