AEM: Attentional Ensemble Model for personalized classifier weight learning

作者：

Highlights：

• Assume each classifier has different predictive ability on different instances.

• Embed classifiers and instances into the same latent space.

• Design an explicit diversity measure of classifiers in the latent space.

• Learn personalized weights of classifiers w.r.t instances for classifier ensemble.

摘要

•Assume each classifier has different predictive ability on different instances.•Embed classifiers and instances into the same latent space.•Design an explicit diversity measure of classifiers in the latent space.•Learn personalized weights of classifiers w.r.t instances for classifier ensemble.

论文关键词：Multiple classifier system,Ensemble learning,Attentional mechanism,Diversity-based learning

论文评审过程：Received 6 November 2018, Revised 8 June 2019, Accepted 15 July 2019, Available online 16 July 2019, Version of Record 27 July 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.106976