Summary of Reliable Conflictive Multi-view Learning, by Cai Xu et al.
Reliable Conflictive Multi-View Learning
by Cai Xu, Jiajun Si, Ziyu Guan, Wei Zhao, Yue Wu, Xiyue Gao
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a novel approach to multi-view learning that tackles real-world data with conflicting information between different views. Existing methods focus on eliminating or replacing conflictive instances, but this is not practical for many applications. The authors introduce the Reliable Conflictive Multi-view Learning (RCML) problem, which requires models to provide decision results and reliabilities for these instances. To address this challenge, they develop an Evidential Conflictive Multi-view Learning (ECML) method that learns view-specific evidence, constructs opinions with reliability estimates, and aggregates them using a conflictive opinion strategy. The authors theoretically prove the effectiveness of ECML and demonstrate its performance on six datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper solves a big problem in machine learning! When we have data from multiple sources, it’s common to find parts that don’t agree. Instead of just getting rid of these disagreements, this paper shows how to deal with them in a way that makes sense. The authors create a new kind of learning method called ECML that can handle these conflicting pieces of information and make good decisions despite the disagreements. They test their method on many different types of data and show it works really well. |
Keywords
* Artificial intelligence * Machine learning
