| Presentation | 2018-03-06 Learning rule-base model by Safe Pattern Pruning Hiroki Kato, Hiroyuki Hanada, Ichiro Takeuchi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We consider learning the prediction model called ''rule-base model''. Rule-base model is the model which uses ''rules'' as explanatory variables. Here a ''rule'' must be described as, for example, ''one's age is 20-29 years old and his/her weight is 70-80kg''. Because the number of rules that can be created from the training data set is enormous by its combinatorial nature, it is difficult to learn the model by using all of them. In this study, we propose a method which can learn rule-base models by converting the learning to the predictive pattern mining problem and using the method called Safe Pattern Pruning (SPP). Furthermore, we confirm its usefulness through numerical experiments. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Rule-base model / Sparse learning / Safe screening / Safe pattern pruning / Empirical risk minimization |
| Paper # | IBISML2017-98 |
| Date of Issue | 2018-02-26 (IBISML) |
| Conference Information | |
| Committee | IBISML |
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| Conference Date | 2018/3/5(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nishijin Plaza, Kyushu University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Statisitical Mathematics, Machine Learning, Data Mining, etc. |
| 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 | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Learning rule-base model by Safe Pattern Pruning |
| Sub Title (in English) | |
| Keyword(1) | Rule-base model |
| Keyword(2) | Sparse learning |
| Keyword(3) | Safe screening |
| Keyword(4) | Safe pattern pruning |
| Keyword(5) | Empirical risk minimization |
| 1st Author's Name | Hiroki Kato |
| 1st Author's Affiliation | Nagoya Institute of Technology(Nagoya Inst. of Tech.) |
| 2nd Author's Name | Hiroyuki Hanada |
| 2nd Author's Affiliation | Nagoya Institute of Technology(Nagoya Inst. of Tech.) |
| 3rd Author's Name | Ichiro Takeuchi |
| 3rd Author's Affiliation | Nagoya Institute of Technology/RIKEN/National Institute for Materials Science(Nagoya Inst. of Tech./RIKEN/NIMS) |
| Date | 2018-03-06 |
| Paper # | IBISML2017-98 |
| Volume (vol) | vol.117 |
| Number (no) | IBISML-475 |
| Page | pp.pp.55-62(IBISML), |
| #Pages | 8 |
| Date of Issue | 2018-02-26 (IBISML) |