Summary of An Incremental Preference Elicitation-based Approach to Learning Potentially Non-monotonic Preferences in Multi-criteria Sorting, by Zhuolin Li et al.
An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting
by Zhuolin Li, Zhen Zhang, Witold Pedrycz
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 novel incremental preference elicitation-based approach introduced in this paper enables decision makers to progressively provide assignment example preference information in multi-criteria sorting (MCS) problems. The max-margin optimization-based model is constructed to model potentially non-monotonic preferences and inconsistent assignment example preference information. An information amount measurement method and question selection strategy are developed to pinpoint the most informative alternative within the framework of uncertainty sampling in active learning. Two incremental preference elicitation-based algorithms are proposed, considering different termination criteria. The approach is applied to a credit rating problem, with computational experiments performed on both artificial and real-world data sets comparing the proposed question selection strategies with several benchmark strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces an innovative way for people to give examples of their preferences in multi-criteria sorting problems. Instead of giving all preference information at once, this approach allows decision makers to provide it incrementally. The method uses a special model that can handle non-monotonic preferences and inconsistent example information. It also includes strategies for selecting the most important questions to ask next. Two different algorithms are developed based on this approach, with different rules for stopping the process. The approach is tested in a credit rating problem and compared to other methods. |
Keywords
» Artificial intelligence » Active learning » Optimization
