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- SURYANTO Chendra Hadi
- Graduate School of Systems and Information Engineering, University of Tsukuba
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- FUKUI Kazuhiro
- Graduate School of Systems and Information Engineering, University of Tsukuba
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- HINO Hideitsu
- Graduate School of Systems and Information Engineering, University of Tsukuba
抄録
Many methods have been proposed for measuring the structural similarity between two protein folds. However, it is difficult to select one best method from them for the classification task, as each method has its own strength and weakness. Intuitively, combining multiple methods is one solution to get the optimal classification results. In this paper, by generalizing the concept of the large margin nearest neighbor (LMNN), a method for combining multiple distance metrics from different types of protein structure comparison methods for protein fold classification task is proposed. While LMNN is limited to Mahalanobis-based distance metric learning from a set of feature vectors of training data, the proposed method learns an optimal combination of metrics from a set of distance metrics by minimizing the distances between intra-class data and enlarging the distances of different classes' data. The main advantage of the proposed method is the capability in finding an optimal weight coefficient for combination of many metrics, possibly including poor metrics, avoiding the difficulties in selecting which metrics to be included for the combination. The effectiveness of the proposed method is demonstrated on classification experiments using two public protein datasets, namely, Ding Dubchak dataset and ENZYMES dataset.
収録刊行物
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E99.D (3), 714-723, 2016
一般社団法人 電子情報通信学会
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キーワード
詳細情報 詳細情報について
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- CRID
- 1390282679354156416
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- NII論文ID
- 130005131822
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- ISSN
- 17451361
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可