Summary of Causal Discovery and Classification Using Lempel-ziv Complexity, by Dhruthi et al.
Causal Discovery and Classification Using Lempel-Ziv Complexity
by Dhruthi, Nithin Nagaraj, Harikrishnan N B
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 paper proposes a novel approach to inferring causal relationships within machine learning algorithms, crucial for achieving explainable AI. The authors introduce a causality measure and distance metric derived from Lempel-Ziv complexity, which enables decision trees to split based on features that strongly cause the outcome. They compare their proposed methods to traditional decision trees using Gini impurity, demonstrating comparable classification performance overall but superior results in datasets generated from causal models. This approach can capture insights beyond classical decision trees, especially in causally structured data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us better understand how machine learning algorithms make decisions. The authors created a new way to measure the strength of relationships between features and the outcome. They tested this method using decision trees and found it works well on certain types of data. This could be important for making AI more transparent, which is useful for many applications. |
Keywords
* Artificial intelligence * Classification * Machine learning
