Becoming More Robust to Label Noise with Classifier Diversity

TL;DR

This paper introduces NICD, a novel noise detection method that uses classifier diversity to improve robustness against label noise across various datasets and algorithms.

Contribution

NICD leverages classifier diversity to create a less biased noise measurement, enhancing noise handling in machine learning models.

Findings

NICD significantly outperforms other noise handling techniques.

NICD is effective across multiple datasets and learning algorithms.

Classifier diversity improves noise detection accuracy.

Abstract

It is widely known in the machine learning community that class noise can be (and often is) detrimental to inducing a model of the data. Many current approaches use a single, often biased, measurement to determine if an instance is noisy. A biased measure may work well on certain data sets, but it can also be less effective on a broader set of data sets. In this paper, we present noise identification using classifier diversity (NICD) -- a method for deriving a less biased noise measurement and integrating it into the learning process. To lessen the bias of the noise measure, NICD selects a diverse set of classifiers (based on their predictions of novel instances) to determine which instances are noisy. We examine NICD as a technique for filtering, instance weighting, and selecting the base classifiers of a voting ensemble. We compare NICD with several other noise handling techniques that do not consider classifier diversity on a set of 54 data sets and 5 learning algorithms. NICD significantly increases the classification accuracy over the other considered approaches and is effective across a broad set of data sets and learning algorithms.