Label Noise Filtering Techniques to Improve Monotonic Classification

TL;DR

This paper introduces a novel noise filtering approach in the preprocessing stage to enhance the monotonicity and accuracy of classifiers in monotonic ordinal classification problems, validated across multiple datasets.

Contribution

It is the first to apply noise filtering algorithms to improve both monotonicity and predictive accuracy in monotonic classification models.

Findings

Noise filtering increases the monotonicity index of models.

The approach improves prediction accuracy across diverse datasets.

Enhanced models better adhere to monotonicity constraints.

Abstract

The monotonic ordinal classification has increased the interest of researchers and practitioners within machine learning community in the last years. In real applications, the problems with monotonicity constraints are very frequent. To construct predictive monotone models from those problems, many classifiers require as input a data set satisfying the monotonicity relationships among all samples. Changing the class labels of the data set (relabelling) is useful for this. Relabelling is assumed to be an important building block for the construction of monotone classifiers and it is proved that it can improve the predictive performance. In this paper, we will address the construction of monotone datasets considering as noise the cases that do not meet the monotonicity restrictions. For the first time in the specialized literature, we propose the use of noise filtering algorithms in a preprocessing stage with a double goal: to increase both the monotonicity index of the models and the accuracy of the predictions for different monotonic classifiers. The experiments are performed over 12 datasets coming from classification and regression problems and show that our scheme improves the prediction capabilities of the monotonic classifiers instead of being applied to original and relabeled datasets. In addition, we have included the analysis of noise filtering process in the particular case of wine quality classification to understand its effect in the predictive models generated.