Summary of Generating Explainable Rule Sets From Tree-ensemble Learning Methods by Answer Set Programming, By Akihiro Takemura et al.
Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming
by Akihiro Takemura, Katsumi Inoue
First submitted to arxiv on: 17 Sep 2021
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 proposed method uses Answer Set Programming (ASP) to generate explainable rule sets from tree-ensemble learners. This decompositional approach leverages the split structures of base decision trees, which are then assessed using pattern mining methods encoded in ASP to extract interesting rules. The approach allows for user-defined constraints and preferences to be represented declaratively, enabling transparent and flexible rule set generation. Rules can be used as explanations to help users better understand models. Experimental evaluation with real-world datasets and popular tree-ensemble algorithms demonstrates the approach’s applicability to a wide range of classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to create understandable rules from machine learning models using something called Answer Set Programming (ASP). They did this by looking at how decision trees are split, which helps them find important rules. These rules can be used as explanations to help people understand why the model made certain predictions. The approach is flexible and allows users to add their own constraints, making it easier to create rules that meet specific needs. |
Keywords
» Artificial intelligence » Classification » Machine learning
