Summary of Xnb: Explainable Class-specific Naive-bayes Classifier, by Jesus S. Aguilar-ruiz et al.
XNB: Explainable Class-Specific NaIve-Bayes Classifier
by Jesus S. Aguilar-Ruiz, Cayetano Romero, Andrea Cicconardi
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 The Explainable Class-Specific Naive Bayes (XNB) classifier is a novel approach that tackles dimensionality reduction by introducing two key innovations: kernel density estimation for accurate posterior probability calculation and class-specific feature subsets. This allows XNB to match the classification performance of traditional Naive Bayes while providing significant improvements in model interpretability. The approach is particularly useful in high-dimensional genomic datasets, where reducing the number of input features is crucial to prevent overfitting and improve accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists created a new way to reduce the amount of data going into a computer program called a classifier. This helps the program make better decisions without getting too complicated. The new method is good at explaining how it makes its decisions, which is important in some fields where understanding why something happened is crucial. |
Keywords
» Artificial intelligence » Classification » Density estimation » Dimensionality reduction » Naive bayes » Overfitting » Probability
