A Flexible and Adaptive Framework for Abstention Under Class Imbalance

In practical applications of machine learning, it is often desirable to identify and abstain on examples where the model's predictions are likely to be incorrect. Much of the prior work on this topic focused on out-of-distribution detection or performance metrics such as top-k accuracy. Comparatively little attention was given to metrics such as area-under-the-curve or Cohen's Kappa, which are extremely relevant for imbalanced datasets. Abstention strategies aimed at top-k accuracy can produce poor results on these metrics when applied to imbalanced datasets, even when all examples are in-distribution. We propose a framework to address this gap. Our framework leverages the insight that calibrated probability estimates can be used as a proxy for the true class labels, thereby allowing us to estimate the change in an arbitrary metric if an example were abstained on. Using this framework, we derive computationally efficient metric-specific abstention algorithms for optimizing the sensitivity at a target specificity level, the area under the ROC, and the weighted Cohen's Kappa. Because our method relies only on calibrated probability estimates, we further show that by leveraging recent work on domain adaptation under label shift, we can generalize to test-set distributions that may have a different class imbalance compared to the training set distribution. On various experiments involving medical imaging, natural language processing, computer vision and genomics, we demonstrate the effectiveness of our approach. Source code available at https://github.com/blindauth/abstention. Colab notebooks reproducing results available at https://github.com/blindauth/abstention_experiments

ReG-Rules: an explainable rule-based ensemble learner for classification

The learning of classification models to predict class labels of new and previously unseen data instances is one of the most essential tasks in data mining. A popular approach to classification is ensemble learning, where a combination of several diverse and independent classification models is used to predict class labels. Ensemble models are important as they tend to improve the average classification accuracy over any member of the ensemble. However, classification models are also often required to be explainable to reduce the risk of irreversible wrong classification. Explainability of classification models is needed in many critical applications such as stock market analysis, credit risk evaluation, intrusion detection, etc. Unfortunately, ensemble learning decreases the level of explainability of the classification, as the analyst would have to examine many decision models to gain insights about the causality of the prediction. The aim of the research presented in this paper is to create an ensemble method that is explainable in the sense that it presents the human analyst with a conditioned view of the most relevant model aspects involved in the prediction. To achieve this aim the authors developed a rule-based explainable ensemble classifier termed Ranked ensemble G-Rules (ReG-Rules) which gives the analyst an extract of the most relevant classification rules for each individual prediction. During the evaluation process ReG-Rules was evaluated in terms of its theoretical computational complexity, empirically on benchmark datasets and qualitatively with respect to the complexity and readability of the induced rule sets. The results show that ReG-Rules scales linearly, delivers a high accuracy and at the same time delivers a compact and manageable set of rules describing the predictions made

Machine Learning with a Reject Option: A survey

Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with rejection recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with rejection. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection, which we carefully formalize. Moreover, we review and categorize strategies to evaluate a model's predictive and rejective quality. Additionally, we define the existing architectures for models with rejection and describe the standard techniques for learning such models. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas

Information-Theoretic Measures for Objective Evaluation of Classifications

This work presents a systematic study of objective evaluations of abstaining classifications using Information-Theoretic Measures (ITMs). First, we define objective measures for which they do not depend on any free parameter. This definition provides technical simplicity for examining "objectivity" or "subjectivity" directly to classification evaluations. Second, we propose twenty four normalized ITMs, derived from either mutual information, divergence, or cross-entropy, for investigation. Contrary to conventional performance measures that apply empirical formulas based on users' intuitions or preferences, the ITMs are theoretically more sound for realizing objective evaluations of classifications. We apply them to distinguish "error types" and "reject types" in binary classifications without the need for input data of cost terms. Third, to better understand and select the ITMs, we suggest three desirable features for classification assessment measures, which appear more crucial and appealing from the viewpoint of classification applications. Using these features as "meta-measures", we can reveal the advantages and limitations of ITMs from a higher level of evaluation knowledge. Numerical examples are given to corroborate our claims and compare the differences among the proposed measures. The best measure is selected in terms of the meta-measures, and its specific properties regarding error types and reject types are analytically derived.Comment: 25 Pages, 1 Figure, 10 Table