Improving Electric Fraud Detection Using Class Imbalance Strategies

Martino, Matías Di; Decia, Federico; Molinelli, Juan; Fernández, Alicia

doi:10.5220/0003768401350141

Cited by 12 publications

(1 citation statement)

References 19 publications

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“…Due to the severity of the risk that an impaired astronaut can pose to a mission's success, identifying individuals in the positive class is of high interest. This issue is also common in applications outside of space behavior, such as in fraud detection or illness detection (Di Martino et al, 2012;Wang et al, 2016;Johnson and Khoshgoftaar, 2019). A popular solution is to assign a different weight to each class, giving higher importance to the minority class to reduce bias toward the majority class.…”

Section: Cross-validation Class Weightingmentioning

confidence: 99%

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

Matar

Gökoğlu

Prelich

et al. 2021

Front. Syst. Neurosci.

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This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 16O, 28Si, 48Ti, or 56Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in attentional set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing machine learning (ML) classifiers such as the Gaussian naïve Bayes, support vector machine, and artificial neural networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreen performance scores as multidimensional input features to predict each rodent’s susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD and CD stages. They, however, do not depict impairment due to 4He in SD and 28Si in CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for more robust predictions of cognitive decrements due to space radiation exposure.

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Section: Cross-validation Class Weightingmentioning

confidence: 99%