Query Based Learning Decision Tree and its Applications in Data Mining
碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 94 === Motivation: Decision tree (DT) is one of the most significant classification methods applied in data mining. By its graphic output, users could have an easy way to interpret the decision flow and the mining outcome. However, the construction of DT is known...
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| Format: | Others |
| Language: | zh-TW |
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2006
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| Online Access: | http://ndltd.ncl.edu.tw/handle/50538707104487115113 |
| Summary: | 碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 94 === Motivation: Decision tree (DT) is one of the most significant classification methods applied in data mining. By its graphic output, users could have an easy way to interpret the decision flow and the mining outcome. However, the construction of DT is known to be time consuming. It will spend a high computation cost when mining the large scale dataset in real world. This drawback causes DT to be ineligible in processing the time critical applications.
Method: In past years, we have introduced the query-based learning (QBL) method to different neural networks for providing a more effective way to achieve good clustering and classification results. We try to apply the QBL concept in DT construction and propose a novel mining scheme called QBLDT.
Achievement: This thesis, in our knowledge, is the first study that applies the QBL concept in DT construction. Experimental results show our proposed QBLDT method is better than the traditional DT construction method in different performance metrics. It makes learning quicker and can achieve better prediction results.
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