Work

Outliers: The Good, the Bad and the Ugly

Year: 2025

Type: article

Abstract: This paper studies the impact of outliers in relational dataset D on the accuracy of ML classifiers M , when D is used to train and evaluate M . Outliers are data points that statistically deviate from the distribution of the majority. We distinguish good outliers, i.e. novel data, from bad ones, i.e. those introduced by errors. Moreover, we separate ugly ones in influential features from the other bad ones. We find that only the ugly ones have negative impact on M , while the good (resp. the other bad) ones have positive (resp. neglectable) impact. To mitigate the negative impact, we propose a class of rules, denoted by OMRs, to identify ugly outliers by embedding ML outlier detectors and statistical functions as predicates. We develop algorithms to (a) learn OMRs from real-life data, and (b) catch and fix ugly outliers using the learned OMRs, instead of removing tuples. Using real-life data, we empirically show that OMRs improve the accuracy of various classifiers by 7.2% on average, up to 34.8%.

Source: Proceedings of the ACM on Management of Data

DOI: 10.1145/3749177

Authors: Shenglin Chen, Wenfei Fan, Ruochun Jin

Institutions: National University of Defense Technology, Beihang University, University of Edinburgh

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: China Association for Science and Technology, National Natural Science Foundation of China, National University of Defense Technology

Awards: G160098265, G2572137687, G3634706652