| Presentation | 2016-07-06 A Semi-supervised Learning Method for Imbalanced Binary Classification Akinori Fujino, Naonori Ueda, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This paper presents a semi-supervised learning method for imbalanced binary classification where the number of positive samples differs largely from that of negative samples. The area under the ROC curve (AUC) is often used as an effective measure for evaluating binary classifiers in such imbalanced tasks, and thus AUC-optimized classifiers have been developed which were trained to maximize an AUC value measured on a labeled sample set. The proposed method utilizes generative models for assisting the incorporation of unlabeled samples in AUC-optimized classifiers. We applied the proposed method to text classification by employing a naive Bayes model as the generative model. Using two benchmark datasets, we confirmed experimentally that the proposed method was more useful for imbalanced binary classification than conventional semi-supervised learning methods based on discriminative, generative, and those hybrid models. We also confirmed the effect of using generative models for semi-supervised learning of AUC-optimized classifiers. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Semi-supervised Learning / AUC Maximization / Generative Model / Naive Bayes Model / Text Classification |
| Paper # | IBISML2016-3 |
| Date of Issue | 2016-06-28 (IBISML) |
| Conference Information | |
| Committee | NC / IPSJ-BIO / IBISML / IPSJ-MPS |
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| Conference Date | 2016/7/4(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 | Shigeo Sato(Tohoku Univ.) / / Kenji Fukumizu(ISM) |
| Vice Chair | Masafumi Hagiwara(Keio Univ.) / / Masashi Sugiyama(Univ. of Tokyo) / Hisashi Kashima(Kyoto Univ.) |
| Secretary | Masafumi Hagiwara(Kyoto Sangyo Univ.) / (Tokyo Inst. of Tech.) / Masashi Sugiyama / Hisashi Kashima(Univ. of Tokyo) / (Nagoya Inst. of Tech.) |
| Assistant | Hisanao Akima(Tohoku Univ.) / Yoshihisa Shinozawa(Keio Univ.) / / Toshihiro Kamishima(AIST) / Tomoharu Iwata(NTT) |
| 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 | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Semi-supervised Learning Method for Imbalanced Binary Classification |
| Sub Title (in English) | |
| Keyword(1) | Semi-supervised Learning |
| Keyword(2) | AUC Maximization |
| Keyword(3) | Generative Model |
| Keyword(4) | Naive Bayes Model |
| Keyword(5) | Text Classification |
| 1st Author's Name | Akinori Fujino |
| 1st Author's Affiliation | Nippon Telegraph and Telephone Corporation(NTT) |
| 2nd Author's Name | Naonori Ueda |
| 2nd Author's Affiliation | Nippon Telegraph and Telephone Corporation(NTT) |
| Date | 2016-07-06 |
| Paper # | IBISML2016-3 |
| Volume (vol) | vol.116 |
| Number (no) | IBISML-121 |
| Page | pp.pp.195-200(IBISML), |
| #Pages | 6 |
| Date of Issue | 2016-06-28 (IBISML) |