The Prediction Aggregating Procedure for Multi-models in Small Dataset Learning

Yaosan Lin, Liangsian Lin, Derchiang Li, Hungyu Chen

研究成果: Conference contribution

摘要

In the past few decades, there were quite a few learning algorithms developed to extract knowledge from data. However, none of the single algorithms can be applicable to learn all the datasets with favor results because data patterns may represent linear and non-linear. Accordingly, the idea of aggregating the predictions of multiple learning models to improve the forecasting accuracy of a single method was proposed. Nevertheless, how to improve the accuracy of the aggregated predictions when learning small datasets is the objective of this study. Based on the distributions of the predictive errors of learning models, the proposed method learns the weights of the models and then tries to aggregate more precise predictions with the weights. The experiment results show the forecasting errors of the predictions aggregated by the proposed method are significantly lower than the predictions of single models and the averaged predictions.

原文English
主出版物標題Proceedings - 2017 International Conference on Computing Intelligence and Information System, CIIS 2017
發行者Institute of Electrical and Electronics Engineers Inc.
頁面79-85
頁數7
ISBN(電子)9781538638866
DOIs
出版狀態Published - 2018 三月 28
事件2017 International Conference on Computing Intelligence and Information System, CIIS 2017 - Nanjing, Jiangsu, China
持續時間: 2017 四月 212017 四月 23

出版系列

名字Proceedings - 2017 International Conference on Computing Intelligence and Information System, CIIS 2017
2018-January

Other

Other2017 International Conference on Computing Intelligence and Information System, CIIS 2017
國家/地區China
城市Nanjing, Jiangsu
期間17-04-2117-04-23

All Science Journal Classification (ASJC) codes

  • 硬體和架構
  • 資訊系統
  • 控制和優化
  • 人工智慧
  • 電腦網路與通信

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