Discrimination and Class Imbalance Aware Online Naive Bayes

TL;DR

This paper introduces a novel, fast Naive Bayes adaptation for fair and accurate online classification in data streams, effectively addressing class imbalance and discrimination issues.

Contribution

It proposes a simple, multi-objective Naive Bayes method with dynamic instance weighting to improve fairness and performance in streaming data classification.

Findings

Outperforms existing fairness-aware methods in discrimination reduction

Achieves high balanced accuracy on various datasets

Handles class imbalance and concept drift effectively

Abstract

Fairness-aware mining of massive data streams is a growing and challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans at critical decision-making points e.g., hiring staff, assessing credit risk, etc. This calls for handling massive incoming information with minimum response delay while ensuring fair and high quality decisions. Recent discrimination-aware learning methods are optimized based on overall accuracy. However, the overall accuracy is biased in favor of the majority class; therefore, state-of-the-art methods mainly diminish discrimination by partially or completely ignoring the minority class. In this context, we propose a novel adaptation of Na\"ive Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance for both the majority and minority classes. Our proposed algorithm is simple, fast, and attains multi-objective optimization goals. To handle class imbalance and concept drifts, a dynamic instance weighting module is proposed, which gives more importance to recent instances and less importance to obsolete instances based on their membership in minority or majority class. We conducted experiments on a range of streaming and static datasets and deduced that our proposed methodology outperforms existing state-of-the-art fairness-aware methods in terms of both discrimination score and balanced accuracy.