| Presentation | 2017-06-24 Positive-Unlabeled Learning with Non-Negative Risk Estimator Ryuichi Kiryo, Gang Niu, Masashi Sugiyama, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | From only emph{positive}~(P) and emph{unlabeled}~(U) data, a binary classifier can be trained with PU learning, in which the state of the art is emph{unbiased PU learning}. However, if its model is very flexible, its empirical risk on training data will go negative and we will suffer from serious overfitting. In this paper, we propose a emph{non-negative risk estimator} for PU learning. When being minimized, it is more robust against overfitting and thus we are able to train very flexible models given limited P data. Moreover, we analyze the emph{bias}, emph{consistency} and emph{mean-squared-error reduction} of the proposed risk estimator and the emph{estimation error} of the corresponding risk minimizer. Experiments show that the proposed risk estimator successfully fixes the overfitting problem of its unbiased counterparts. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Supervised Learning / Classification / Positive-Unlabeled Learning / PU Learning |
| Paper # | IBISML2017-4 |
| Date of Issue | 2017-06-17 (IBISML) |
| Conference Information | |
| Committee | NC / IPSJ-BIO / IBISML / IPSJ-MPS |
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| Conference Date | 2017/6/23(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Institute of Science and Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Machine Learning Approach to Biodata Mining, and General |
| Chair | Masafumi Hagiwara(Keio Univ.) / / Kenji Fukumizu(ISM) |
| Vice Chair | Yutaka Hirata(Chubu Univ.) / / Masashi Sugiyama(Univ. of Tokyo) |
| Secretary | Yutaka Hirata(Tokyo Inst. of Tech.) / (Nagoya Univ.) / Masashi Sugiyama / (Kyoto Univ.) |
| Assistant | Yoshihisa Shinozawa(Keio Univ.) / Keiichiro Inagaki(Chubu Univ.) / / Ichiro Takeuchi(Nagoya Inst. of Tech.) / Toshihiro Kamishima(AIST) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Special Interest Group on Bioinformatics and Genomics / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / Special Interest Group on Mathematical Modeling and Problem Solving |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Positive-Unlabeled Learning with Non-Negative Risk Estimator |
| Sub Title (in English) | |
| Keyword(1) | Supervised Learning |
| Keyword(2) | Classification |
| Keyword(3) | Positive-Unlabeled Learning |
| Keyword(4) | PU Learning |
| 1st Author's Name | Ryuichi Kiryo |
| 1st Author's Affiliation | The University of Tokyo/RIKEN(Univ. of Tokyo/RIKEN) |
| 2nd Author's Name | Gang Niu |
| 2nd Author's Affiliation | The University of Tokyo(Univ. of Tokyo) |
| 3rd Author's Name | Masashi Sugiyama |
| 3rd Author's Affiliation | RIKEN/The University of Tokyo(RIKEN/Univ. of Tokyo) |
| Date | 2017-06-24 |
| Paper # | IBISML2017-4 |
| Volume (vol) | vol.117 |
| Number (no) | IBISML-110 |
| Page | pp.pp.63-70(IBISML), |
| #Pages | 8 |
| Date of Issue | 2017-06-17 (IBISML) |