抄録
Multi-label classification (MLC) is the technique to solve conditional problem where decisions is a set of labels. Classification is the learning task by using historical examples to make model of conditions to make decision of unseen example. To improve decision in MLC. We can use advantage of domination analysis or dependency relations between the classes by using enhanced Bayesian Chain Classifier (BCC). We introduce an approach for chaining classifier primary order by its individual label accuracy priority (LPC-CC). Our method considers the dependencies among label based on label accuracy priority ordering. Thus, Binary Relevance (BR) theoretical is used for label sequencing priority, and cycle classifiers chain using nave Bayes is for finding domination. The model have been tested on 2 well-known benchmark datasets named Yeast and Emotions and a collection of car sell records from a Thailand Automotive Company.
本文言語 | English |
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ホスト出版物のタイトル | Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 |
出版社 | Institute of Electrical and Electronics Engineers Inc. |
ページ | 1048-1053 |
ページ数 | 6 |
ISBN(電子版) | 9781467389853 |
DOI | |
出版ステータス | Published - 2016 8月 31 |
イベント | 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan 継続期間: 2016 7月 10 → 2016 7月 14 |
Other
Other | 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 |
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国/地域 | Japan |
City | Kumamoto |
Period | 16/7/10 → 16/7/14 |
ASJC Scopus subject areas
- 情報システム
- コンピュータ ネットワークおよび通信
- コンピュータ サイエンスの応用
- コンピュータ ビジョンおよびパターン認識