| Presentation | 2021-03-05 A Model Selection Optimization Method for Distributed Machine Learning with Feature Model Combination Ryuichi Mochizuki, Takeshi Tsuchiya, Hiroo Hirose, Tetsuyasu Yamada, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This study clarifies the optimal feature model selection method in data analysis under the environment where the feature models are distributed among many nodes of a network. Only the effective feature models are selected from a multiple nodes, can be merging to the differences in the characteristics of each model, which are caused by the characteristics of the source data. In the proposed method, the nodes are selected based on the accuracy of data analysis required by the client, synchronizing the information cooperatively among the nodes. In order to evaluate the effectiveness of feature model selection, we compared the task accuracy of training and merging using multiple data sets. In addition, the effectiveness of sequential merging of feature models based on similarity is clarified. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Machine Learning / Distributed Information Platform / Feature model selection / Feature model merging |
| Paper # | IN2020-83 |
| Date of Issue | 2021-02-25 (IN) |
| Conference Information | |
| Committee | IN / NS |
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| Conference Date | 2021/3/4(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | General |
| Chair | Kenji Ishida(Hiroshima City Univ.) / Akihiro Nakao(Univ. of Tokyo) |
| Vice Chair | Kunio Hato(Internet Multifeed) / Tetsuya Oishi(NTT) |
| Secretary | Kunio Hato(Hiroshima City Univ.) / Tetsuya Oishi(KDDI Research) |
| Assistant | / Shinya Kawano(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Information Networks / Technical Committee on Network Systems |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Model Selection Optimization Method for Distributed Machine Learning with Feature Model Combination |
| Sub Title (in English) | |
| Keyword(1) | Machine Learning |
| Keyword(2) | Distributed Information Platform |
| Keyword(3) | Feature model selection |
| Keyword(4) | Feature model merging |
| 1st Author's Name | Ryuichi Mochizuki |
| 1st Author's Affiliation | Suwa University of Science(SUS) |
| 2nd Author's Name | Takeshi Tsuchiya |
| 2nd Author's Affiliation | Suwa University of Science(SUS) |
| 3rd Author's Name | Hiroo Hirose |
| 3rd Author's Affiliation | Suwa University of Science(SUS) |
| 4th Author's Name | Tetsuyasu Yamada |
| 4th Author's Affiliation | Suwa University of Science(SUS) |
| Date | 2021-03-05 |
| Paper # | IN2020-83 |
| Volume (vol) | vol.120 |
| Number (no) | IN-414 |
| Page | pp.pp.172-177(IN), |
| #Pages | 6 |
| Date of Issue | 2021-02-25 (IN) |