ArticleGenetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference2008
A Balanced Accuracy Fitness Function Leads to Robust Analysis using Grammatical Evolution Neural Networks in the Case of Class Imbalance.
Article in Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation.BioData mining · 2021Article
- Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network.Frontiers in bioengineering and biotechnology · 2021Article
- The power metric: a new statistically robust enrichment-type metric for virtual screening applications with early recovery capability.Journal of cheminformatics · 2017Article
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Authors and funding
6 authors.
Funding
Abstract
Grammatical Evolution Neural Networks (GENN) is a computational method designed to detect gene-gene interactions in genetic epidemiology, but has so far only been evaluated in situations with balanced numbers of cases and controls. Real data, however, rarely has such perfectly balanced classes. In the current study, we test the power of GENN to detect interactions in data with a range of class imbalance using two fitness functions (classification error and balanced error), as well as data re-sampling. We show that when using classification error, class imbalance greatly decreases the power of GENN. Re-sampling methods demonstrated improved power, but using balanced accuracy resulted in the highest power. Based on the results of this study, balanced error has replaced classification error in the GENN algorithm.
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Registered trials
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