<ul data-eligibleForWebStory="true"><li>This study focuses on addressing imbalanced datasets, particularly in the context of 'Liking' label detection in the DEAP dataset used in neuroscience, cognitive science, and medical diagnostics.</li><li>Class imbalance issues in data analysis, where minority classes are vital, have been typically overlooked by previous research focusing on more balanced labels like arousal and valence.</li><li>The study utilizes numerical optimization techniques to maximize the area under the curve (AUC) as a metric for enhancing minority class detection.</li><li>Comparisons are made between the proposed linear classifier approach and traditional models like logistic regression and support vector machines (SVM).</li><li>The new method significantly outperforms traditional models, boosting recall from 41.6% to 79.7% and increasing the F1-score from 0.506 to 0.632.</li><li>These findings demonstrate the effectiveness of AUC maximization through numerical optimization in dealing with imbalanced datasets to improve predictive accuracy for critical minority classes in unseen data.</li></ul>

Electroencephalogram Emotion Recognition via AUC Maximization

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