講演抄録/キーワード |
講演名 |
2020-03-11 10:45
Calibrated Surrogate Maximization of Linear-Fractional Utility in Binary Classification ○Han Bao(Univ. of Tokyo/RIKEN)・Masashi Sugiyama(RIKEN/Univ. of Tokyo) IBISML2019-43 |
抄録 |
(和) |
Complex classification performance metrics such as the F-measure and Jaccard index are often used to handle class imbalance. They are not endowed with M-estimation, which makes optimization hard. We consider a family named linear-fractional metrics and propose methods to directly maximize performance objectives via a calibrated surrogate, which is a tractable yet consistent lower-bound of the original objectives. |
(英) |
Complex classification performance metrics such as the F-measure and Jaccard index are often used to handle class imbalance. They are not endowed with M-estimation, which makes optimization hard. We consider a family named linear-fractional metrics and propose methods to directly maximize performance objectives via a calibrated surrogate, which is a tractable yet consistent lower-bound of the original objectives. |
キーワード |
(和) |
/ / / / / / / |
(英) |
binary classification / F-measure / Jaccard index / surrogate loss / classification calibration / calibrated surrogate loss / / |
文献情報 |
信学技報, vol. 119, no. 476, IBISML2019-43, pp. 71-78, 2020年3月. |
資料番号 |
IBISML2019-43 |
発行日 |
2020-03-03 (IBISML) |
ISSN |
Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
PDFダウンロード |
IBISML2019-43 |