Data imbalance in classification: Experimental evaluation
By Fadi A. Thabtah, Suhel Hammoud, Firuz Kamalov, Amanda Gonsalves. Information Sciences, 2020
773 citations recorded by the source corpus.
Abstract The advent of Big Data has ushered a new era of scientific breakthroughs. One of the common issues that affects raw data is class imbalance problem which refers to imbalanced distribution of values of the response variable. This issue is present in fraud detection, network intrusion detection, medical diagnostics, and a number of other fields where negatively labeled instances significantly outnumber positively labeled instances. Modern machine learning techniques struggle to deal with imbalanced data by focusing on minimizing the error rate for the majority class while ignoring the minority class. The goal of our paper is demonstrate the effects of class imbalance on classification models. Concretely, we study the impact of varying class imbalance ratios on classifier accuracy. By highlighting the precise nature of the relationship between the degree of class imbalance and the corresponding effects on classifier performance we hope to help researchers to better tackle the problem. To this end, we carry out extensive experiments using 10-fold cross validation on a large number of datasets. In particular, we determine that the relationship between the class imbalance ratio and the accuracy is convex.
Subfields: Accounting, Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/ce1b627b8244a62bab9c17dbc6f255c063ac59ca
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In Artificial Intelligence
In Electrical and Electronic Engineering
Citations in the corpus
- A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data (2021, background): “In contrast, True Negative (TN) is the number of correctly predicted negative instances [69]–[71].”
- A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning (2023, neutral): “Thabtah et al. (2020) provide another in-depth analysis of imbalance ratio and its effect on classifier accuracy using large scale experimental analysis.”