Summary of Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregation, by Long Shi et al.
Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregation
by Long Shi, Chuanqing Tang, Huangyi Deng, Cai Xu, Lei Xing, Badong Chen
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed generalized trusted multi-view classification framework introduces a novel hierarchical opinion aggregation approach for trustworthy decision-making. Building upon the Trusted Multi-view Classification (TMC) framework by Han et al., this method incorporates intra-view and inter-view aggregations to leverage both common and specific information across views. The intra-view phase eliminates feature noise, while the inter-view attention mechanism facilitates evidence-level opinion aggregation from different views. This approach outperforms state-of-the-art trust-related baselines in extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of making trusted decisions is being developed by combining information from multiple sources. Currently, most methods only consider how to combine this information, but they don’t use the valuable details within each source. The proposed method looks at both what’s common across sources and what’s unique about each one. It then uses this information to improve the quality of each source before combining them. This approach is a pioneering effort in trusted decision-making and has shown better results than other methods. |
Keywords
* Artificial intelligence * Attention * Classification
