Directly Handling Missing Data in Linear Discriminant Analysis for Enhancing Classification Accuracy and Interpretability

TL;DR

This paper introduces WLDA, a new method for linear discriminant analysis that directly handles missing data without imputation, improving classification accuracy and interpretability in incomplete datasets.

Contribution

The paper proposes WLDA, a novel extension of LDA that incorporates a weight matrix to manage missing data directly, enhancing performance and interpretability.

Findings

WLDA outperforms traditional methods in datasets with missing data.

It maintains interpretability while improving classification accuracy.

Experimental results show consistent improvements across various datasets.

Abstract

As the adoption of Artificial Intelligence (AI) models expands into critical real-world applications, ensuring the explainability of these models becomes paramount, particularly in sensitive fields such as medicine and finance. Linear Discriminant Analysis (LDA) remains a popular choice for classification due to its interpretable nature, derived from its capacity to model class distributions and enhance class separation through linear combinations of features. However, real-world datasets often suffer from incomplete data, posing substantial challenges for both classification accuracy and model interpretability. In this paper, we introduce a novel and robust classification method, termed Weighted missing Linear Discriminant Analysis (WLDA), which extends LDA to handle datasets with missing values without the need for imputation. Our approach innovatively incorporates a weight matrix that penalizes missing entries, thereby refining parameter estimation directly on incomplete data. This methodology not only preserves the interpretability of LDA but also significantly enhances classification performance in scenarios plagued by missing data. We conduct an in-depth theoretical analysis to establish the properties of WLDA and thoroughly evaluate its explainability. Experimental results across various datasets demonstrate that WLDA consistently outperforms traditional methods, especially in challenging environments where missing values are prevalent in both training and test datasets. This advancement provides a critical tool for improving classification accuracy and maintaining model transparency in the face of incomplete data.