書誌事項
- タイトル別名
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- Two Step Decision Tree for Polymer Discrimination
- ポリマー ハンベツ ノ タメ ノ 2 ダンカイ ハンベツ ケッテイギ
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This paper proposes a novel method for generating a decision tree to discriminate polymers accurately with the near-infrared rays spectrum. The polymer discrimination system is needed for recycling plastics, and the near-infrared rays spectrum is useful for rapid and non-destructive discrimination. The former system SESAT, which is based on symbiotic evolution, can generate simple and accurate trees, but is not effective for data that has a lot of attributes like the near-infrared rays spectrum. We design the structure of the partial solution ``sprig'' for sufficient learning, and the fitness function of the whole solution ``decision tree blueprint'' for 2-class discrimination. In addition, we introduce two-step discrimination with the aim of obtaining higher accuracy. In the first step, examples are divided into two groups, one group being easier than the other to discriminate by a tree. In the second step, two trees are generated that discriminate one kind of polymer from the others, for two groups of examples. By doing this, a minority of examples is also discriminated accurately. Based on this method we developed a polymer discrimination system called TS-SEPT. Our experimental results on real data of polymers show that the accuracy of TS-SEPT compares favorably with that of the other systems, the similar system without two-step discrimination, SESAT and C5.0. It emerged that both the method for generating decision trees and two-step discrimination contributed to the improved accuracy.
収録刊行物
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- 人工知能学会論文誌
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人工知能学会論文誌 21 295-300, 2006
一般社団法人 人工知能学会
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詳細情報 詳細情報について
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- CRID
- 1390001205106711552
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- NII論文ID
- 10022006424
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- NII書誌ID
- AA11579226
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- ISSN
- 13468030
- 13460714
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- NDL書誌ID
- 8686481
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
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- 使用不可