# Induction of Selective Bayesian Classifiers

arXiv: https://arxiv.org/abs/1302.6828 · HTML: https://mlsanity.com/paper/1302.6828/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2013.

## Built on by (most cited first, 7 of 7)

- [Fractional Naive Bayes (FNB): non-convex optimization for a parsimonious weighted selective naive Bayes classifier](https://mlsanity.com/paper/2409.11100/) (2024)
- [An Efficient Shapley Value Computation for the Naive Bayes Classifier](https://mlsanity.com/paper/2307.16718/) (2023)
- [Viewing the process of generating counterfactuals as a source of knowledge: a new approach for explaining classifiers](https://mlsanity.com/paper/2309.04284/) (2023)
- [Case Study on Detecting COVID-19 Health-Related Misinformation in Social Media](https://mlsanity.com/paper/2106.06811/) (2021)
- [Bayes Classification using an approximation to the Joint Probability Distribution of the Attributes](https://mlsanity.com/paper/2205.14779/) (2022)
- [Optimal partition of feature using Bayesian classifier](https://mlsanity.com/paper/2304.14537/) (2023)
- [Variable selection for Na"ive Bayes classification](https://mlsanity.com/paper/2401.18039/) (2024)

## Abstract (arXiv)

In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that c arries out a greedy search through the space of features. We hypothesize that this approach will improve asymptotic accuracy in domains that involve correlated features without reducing the rate of learning in ones that do not. We report experimental results on six natural domains, including comparisons with decision-tree induction, that support these hypotheses. In closing, we discuss other approaches to extending naive Bayesian classifiers and outline some directions for future research.