WEKO3
アイテム
Multiclass Visual Classifier Based on Bipartite Graph Representation of Decision Tables
http://hdl.handle.net/10252/5198
http://hdl.handle.net/10252/519876cccbc1-39ab-40bb-ba93-614e2a66d74a
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
Proc.LION4(LNCS 6073)_169-183.pdf (732.6 kB)
|
|
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2013-11-20 | |||||
| タイトル | ||||||
| タイトル | Multiclass Visual Classifier Based on Bipartite Graph Representation of Decision Tables | |||||
| 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_6501 | |||||
| 資源タイプ | journal article | |||||
| 著者 |
Haraguchi, Kazuya
× Haraguchi, Kazuya× Hong, Seok-Hee× Nagamochi, Hiroshi |
|||||
| 著者別名 | ||||||
| 識別子Scheme | WEKO | |||||
| 識別子 | 10009 | |||||
| 姓名 | 原口, 和也 | |||||
| 言語 | ja | |||||
| 書誌情報 |
en : Lecture Notes in Computer Science 巻 6073, p. 169-183, 発行日 2010 |
|||||
| 出版者 | ||||||
| 出版者 | Springer Berlin Heidelberg | |||||
| 言語 | en | |||||
| ISSN / EISSN | ||||||
| 収録物識別子タイプ | PISSN | |||||
| 収録物識別子 | 0302-9743 | |||||
| DOI | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | info:doi/10.1007/978-3-642-13800-3_13 | |||||
| 出版社版URI | ||||||
| 言語 | ja | |||||
| 権利情報 | http://link.springer.com/chapter/10.1007%2F978-3-642-13800-3_13 | |||||
| 著作権注記 | ||||||
| 言語 | en | |||||
| 権利情報 | The original publication is available at www.springerlink.com | |||||
| テキストバージョン | ||||||
| 出版タイプ | AM | |||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
| 日本十進分類法 | ||||||
| 言語 | ja | |||||
| 主題Scheme | NDC | |||||
| 主題 | 007 | |||||
| NIIサブジェクト | ||||||
| 言語 | ja | |||||
| 主題Scheme | Other | |||||
| 主題 | 情報学 | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In this paper, we consider K-class classification problem, a significant issue in machine learning or artificial intelligence. In this problem, we are given a training set of samples, where each sample is represented by a nominal-valued vector and is labeled as one of the predefined K classes. The problem asks to construct a classifier that predicts the classes of future samples with high accuracy. For K = 2, we have studied a new visual classifier named 2-class SE-graph based classifier (2-SEC) in our previous works, which is constructed as follows: We first create several decision tables from the training set and extract a bipartite graph called an SE-graph that represents the relationship between the training set and the decision tables. We draw the SE-graph as a twolayered drawing by using an edge crossing minimization technique, and the resulting drawing acts as a visual classifier. We can extend 2-SEC to K-SEC for K > 2 naturally, but this extension does not consider the relationship between classes, and thus may perform badly on some data sets. In this paper, we propose SEC-TREE classifier for K > 2, which decomposes the given K-class problem into subproblems for fewer classes. Following our philosophy, we employ edge crossing minimization technique for this decomposition. Compared to previous decomposition strategies, SEC-TREE can extract any tree as the subproblem hierarchy. In computational studies, SEC-TREE outperforms C4.5 and is competitive with SVM especially when K is large. | |||||
| 言語 | en | |||||
