ASCII: ASsisted Classification with Ignorance Interchange
- URL: http://arxiv.org/abs/2010.10747v1
- Date: Wed, 21 Oct 2020 03:57:36 GMT
- Title: ASCII: ASsisted Classification with Ignorance Interchange
- Authors: Jiaying Zhou, Xun Xian, Na Li, Jie Ding
- Abstract summary: We propose a method named ASCII for an agent to improve its classification performance through assistance from other agents.
The main idea is to iteratively interchange an ignorance value between 0 and 1 for each collated sample among agents.
The method is naturally suitable for privacy-aware, transmission-economical, and decentralized learning scenarios.
- Score: 17.413989127493622
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rapid development in data collecting devices and computation platforms
produces an emerging number of agents, each equipped with a unique data
modality over a particular population of subjects. While the predictive
performance of an agent may be enhanced by transmitting other data to it, this
is often unrealistic due to intractable transmission costs and security
concerns. While the predictive performance of an agent may be enhanced by
transmitting other data to it, this is often unrealistic due to intractable
transmission costs and security concerns. In this paper, we propose a method
named ASCII for an agent to improve its classification performance through
assistance from other agents. The main idea is to iteratively interchange an
ignorance value between 0 and 1 for each collated sample among agents, where
the value represents the urgency of further assistance needed. The method is
naturally suitable for privacy-aware, transmission-economical, and
decentralized learning scenarios. The method is also general as it allows the
agents to use arbitrary classifiers such as logistic regression, ensemble tree,
and neural network, and they may be heterogeneous among agents. We demonstrate
the proposed method with extensive experimental studies.
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