| Item type |
会議発表論文 / Conference Paper(1) |
| 公開日 |
2015-08-31 |
| タイトル |
|
|
タイトル |
Semantic classification of spacecraft's status: integrating system intelligence and human knowledge |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
|
資源タイプ |
conference paper |
| アクセス権 |
|
|
アクセス権 |
metadata only access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_14cb |
| 著者 |
Sakurada, Mayu
Yairi, Takehisa
Nakajima, Yuta
Nishimura, Naoki
Parkikh, Devi
|
| 著者所属（英） |
|
|
|
en |
|
|
The University of Tokyo |
| 著者所属（英） |
|
|
|
en |
|
|
The University of Tokyo |
| 著者所属（英） |
|
|
|
en |
|
|
Japan Aerospace Exploration Agency(JAXA) |
| 著者所属（英） |
|
|
|
en |
|
|
Japan Aerospace Exploration Agency(JAXA) |
| 著者所属（英） |
|
|
|
en |
|
|
Virginia Tech |
| 出版者（英） |
|
|
出版者 |
Institute of Electrical and Electronics Engineers, Inc.(IEEE) |
| 書誌情報 |
en : Semantic Computing (ICSC), 2015 IEEE International Conference on
p. 81-84,
発行日 2015-02
|
| 会議概要（会議名, 開催地, 会期, 主催者等）（英） |
|
|
内容記述タイプ |
Other |
|
内容記述 |
2015 IEEE International Conference on Semantic Computing(ICSC2015) (February 7-9, 2015.), Anaheim, California, USA |
| 抄録（英） |
|
|
内容記述タイプ |
Other |
|
内容記述 |
In this paper, we introduce a novel approach where the system involves human knowledge in the classification task using decision trees. Machine learning techniques are now applied to a variety of tasks in real-world problems. The computer performs complex computations better than humans. However, in many real-world applications, humans have background domain knowledge about the problem that the computer often does not have. For instance, in a spacecraft status classification task, humans have a sense for which factors are likely to correlate with the classes of interest. Without this knowledge, machines may overfit to training data. We propose to combine two models: one based on human reasoning, common sense, or heuristics, and the other learned by a machine learning algorithm in a data-driven manner. In our experiments, we use decision trees and categorical features so that the model consists of rules which are semantic and interpretable for humans. Our proposed approach results in an improvement in classification performance over either models alone. Our work illustrates the possibility of integrating human knowledge and artificial intelligence. |
| ISBN |
|
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|
識別子タイプ |
ISBN |
|
|
関連識別子 |
978-1-4799-7936-3 |
| DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
http://dx.doi.org/10.1109/ICOSC.2015.7050783 |
|
|
関連名称 |
info:doi/10.1109/ICOSC.2015.7050783 |
| 資料番号 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
資料番号: PA1510080000 |
