Local Probabilistic Model for Bayesian Classification: a Generalized Local Classification Model

TL;DR

This paper introduces a local probabilistic modeling approach for Bayesian classification, which simplifies the modeling process by focusing on local regions, thereby relaxing global assumptions and improving classification effectiveness.

Contribution

It proposes a novel local probabilistic model for Bayesian classification that adapts to local data regions, offering a flexible and potentially more accurate classification framework.

Findings

Effective on real-world datasets

Relaxed assumptions improve model flexibility

Enhanced classification accuracy

Abstract

In Bayesian classification, it is important to establish a probabilistic model for each class for likelihood estimation. Most of the previous methods modeled the probability distribution in the whole sample space. However, real-world problems are usually too complex to model in the whole sample space; some fundamental assumptions are required to simplify the global model, for example, the class conditional independence assumption for naive Bayesian classification. In this paper, with the insight that the distribution in a local sample space should be simpler than that in the whole sample space, a local probabilistic model established for a local region is expected much simpler and can relax the fundamental assumptions that may not be true in the whole sample space. Based on these advantages we propose establishing local probabilistic models for Bayesian classification. In addition, a Bayesian classifier adopting a local probabilistic model can even be viewed as a generalized local classification model; by tuning the size of the local region and the corresponding local model assumption, a fitting model can be established for a particular classification problem. The experimental results on several real-world datasets demonstrate the effectiveness of local probabilistic models for Bayesian classification.