講演名 | 2017-06-24 Positive-Unlabeled Learning with Non-Negative Risk Estimator 木了 龍一(東大/理研), ニウ ガン(東大), 杉山 将(理研/東大), |
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抄録(和) | 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. |
抄録(英) | 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. |
キーワード(和) | 教師付き学習 / 分類問題 / PU学習 |
キーワード(英) | Supervised Learning / Classification / Positive-Unlabeled Learning / PU Learning |
資料番号 | IBISML2017-4 |
発行日 | 2017-06-17 (IBISML) |
研究会情報 | |
研究会 | NC / IPSJ-BIO / IBISML / IPSJ-MPS |
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開催期間 | 2017/6/23(から3日開催) |
開催地(和) | 沖縄科学技術大学院大学 |
開催地(英) | Okinawa Institute of Science and Technology |
テーマ(和) | 機械学習によるバイオデータマインニング、一般 |
テーマ(英) | Machine Learning Approach to Biodata Mining, and General |
委員長氏名(和) | 萩原 将文(慶大) / / 福水 健次(統計数理研) |
委員長氏名(英) | Masafumi Hagiwara(Keio Univ.) / / Kenji Fukumizu(ISM) |
副委員長氏名(和) | 平田 豊(中部大) / / 杉山 将(東大) |
副委員長氏名(英) | Yutaka Hirata(Chubu Univ.) / / Masashi Sugiyama(Univ. of Tokyo) |
幹事氏名(和) | 青西 亨(東工大) / 吉川 大弘(名大) / / 鹿島 久嗣(京大) / 津田 宏治(東大) |
幹事氏名(英) | Toru Aonishi(Tokyo Inst. of Tech.) / Tomohiro Yoshikawa(Nagoya Univ.) / / Hisashi Kashima(Kyoto Univ.) / Koji Tsuda(Univ. of Tokyo) |
幹事補佐氏名(和) | 篠沢 佳久(慶大) / 稲垣 圭一郎(中部大) / / 竹内 一郎(名工大) / 神嶌 敏弘(産総研) |
幹事補佐氏名(英) | Yoshihisa Shinozawa(Keio Univ.) / Keiichiro Inagaki(Chubu Univ.) / / Ichiro Takeuchi(Nagoya Inst. of Tech.) / Toshihiro Kamishima(AIST) |
講演論文情報詳細 | |
申込み研究会 | 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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本文の言語 | ENG |
タイトル(和) | |
サブタイトル(和) | |
タイトル(英) | Positive-Unlabeled Learning with Non-Negative Risk Estimator |
サブタイトル(和) | |
キーワード(1)(和/英) | 教師付き学習 / Supervised Learning |
キーワード(2)(和/英) | 分類問題 / Classification |
キーワード(3)(和/英) | PU学習 / Positive-Unlabeled Learning |
キーワード(4)(和/英) | / PU Learning |
第 1 著者 氏名(和/英) | 木了 龍一 / Ryuichi Kiryo |
第 1 著者 所属(和/英) | 東京大学/理化学研究所(略称:東大/理研) The University of Tokyo/RIKEN(略称:Univ. of Tokyo/RIKEN) |
第 2 著者 氏名(和/英) | ニウ ガン / Gang Niu |
第 2 著者 所属(和/英) | 東京大学(略称:東大) The University of Tokyo(略称:Univ. of Tokyo) |
第 3 著者 氏名(和/英) | 杉山 将 / Masashi Sugiyama |
第 3 著者 所属(和/英) | 理化学研究所/東京大学(略称:理研/東大) RIKEN/The University of Tokyo(略称:RIKEN/Univ. of Tokyo) |
発表年月日 | 2017-06-24 |
資料番号 | IBISML2017-4 |
巻番号(vol) | vol.117 |
号番号(no) | IBISML-110 |
ページ範囲 | pp.63-70(IBISML), |
ページ数 | 8 |
発行日 | 2017-06-17 (IBISML) |