Optimal Deduction Of Decision Trees For Machine Learning In Artificial Intelligence
碩士 === 國立交通大學 === 資訊管理研究所 === 82 === The algorithms for building knowledge-based systems by inductive inference from example have been demonstrated successfully in several researches. The ID3 algorithm is a well-known approach (proposed by J.R. Quinlan) to synthesizing decision trees that has bee...
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| Format: | Others |
| Language: | en_US |
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1994
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| Online Access: | http://ndltd.ncl.edu.tw/handle/89463215068528055476 |
| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 82 ===
The algorithms for building knowledge-based systems by inductive inference from example have been demonstrated successfully in several researches. The ID3 algorithm is a well-known approach (proposed by J.R. Quinlan) to synthesizing decision trees that has been used in a variety of systems. But it lacks of powerful expression and ignores the uncertain data.
This thesis proposes a new algorithm (named C-MAX algorithm) to establish decision trees by using 0-1 integer programming, and it can be further utilized to construct one-level trees. Meanwhile, the treatment for uncertain data is also considered in this new algorithm. Finally, this thesis provides the comparison between ID3 and C-MAX algorithm to show the size of decision trees can be much reduced as well as the accuracy and coverage rate can be upgraded.
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