| Presentation | 2017-11-09 Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning Tomoya Sakai, Gang Niu, Masashi Sugiyama, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification. So far, various supervised AUC optimization methods have been developed and they are also extended to semi-supervised scenarios to cope with small sample problems. However, existing semi-supervised AUC optimization methods rely on strong distributional assumptions, which are rarely satisfied in real-world problems. In this paper, we propose a novel semi-supervised AUC optimization method that does not require such restrictive assumptions. We first develop an AUC optimization method based only on positive and unlabeled data (PU-AUC) and then extend it to semi-supervised learning by combining it with a supervised AUC optimization method. We theoretically prove that, without the restrictive distributional assumptions, unlabeled data contribute to improving the generalization performance in PU and semi-supervised AUC optimization methods. Finally, we demonstrate the practical usefulness of the proposed methods through experiments. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Semi-Supervised LearningPU LearningAUC OptimizationClassification |
| Paper # | IBISML2017-40 |
| Date of Issue | 2017-11-02 (IBISML) |
| Conference Information | |
| Committee | IBISML |
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| Conference Date | 2017/11/8(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Univ. of Tokyo |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Information-Based Induction Science Workshop (IBIS2017) |
| Chair | Kenji Fukumizu(ISM) |
| Vice Chair | Masashi Sugiyama(Univ. of Tokyo) / Hisashi Kashima(Kyoto Univ.) |
| Secretary | Masashi Sugiyama(Nagoya Inst. of Tech.) / Hisashi Kashima(Univ. of Tokyo) |
| Assistant | Tomoharu Iwata(NTT) / Toshihiro Kamishima(AIST) |
| Paper Information | |
| Registration To | Technical Committee on Infomation-Based Induction Sciences and Machine Learning |
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| Language | ENG-JTITLE |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning |
| Sub Title (in English) | |
| Keyword(1) | Semi-Supervised LearningPU LearningAUC OptimizationClassification |
| 1st Author's Name | Tomoya Sakai |
| 1st Author's Affiliation | The University of Tokyo/RIKEN(UTokyo/RIKEN) |
| 2nd Author's Name | Gang Niu |
| 2nd Author's Affiliation | The University of Tokyo/RIKEN(UTokyo/RIKEN) |
| 3rd Author's Name | Masashi Sugiyama |
| 3rd Author's Affiliation | RIKEN/The University of Tokyo(RIKEN/UTokyo) |
| Date | 2017-11-09 |
| Paper # | IBISML2017-40 |
| Volume (vol) | vol.117 |
| Number (no) | IBISML-293 |
| Page | pp.pp.39-46(IBISML), |
| #Pages | 8 |
| Date of Issue | 2017-11-02 (IBISML) |