# A robust approach to model-based classification based on trimming and constraints

## TL;DR

This paper introduces a robust model-based classification method that employs trimming and eigenvalue constraints to improve classification accuracy in contaminated datasets, especially when training data is limited.

## Contribution

It presents a novel robust modification to the Model-Based Classification framework using trimming and eigenvalue constraints, enhancing noise handling and reliability.

## Key findings

- Effective in handling noise in response and explanatory variables
- Provides reliable classification with contaminated datasets
- Demonstrates benefits through experiments on real and simulated data

## Abstract

In a standard classification framework a set of trustworthy learning data are employed to build a decision rule, with the final aim of classifying unlabelled units belonging to the test set. Therefore, unreliable labelled observations, namely outliers and data with incorrect labels, can strongly undermine the classifier performance, especially if the training size is small. The present work introduces a robust modification to the Model-Based Classification framework, employing impartial trimming and constraints on the ratio between the maximum and the minimum eigenvalue of the group scatter matrices. The proposed method effectively handles noise presence in both response and exploratory variables, providing reliable classification even when dealing with contaminated datasets. A robust information criterion is proposed for model selection. Experiments on real and simulated data, artificially adulterated, are provided to underline the benefits of the proposed method.