Improved chicken swarm optimization with RNNs for multi-label classification in imbalanced data

Abstract

Multi-label classification (MLC) seeks to make multiple predictions for an instance by identifying associations between labels, thereby enhancing prediction capability. The paper presents a new method for combining improved chicken swarm optimization (ICSO) feature selection with recurrent neural networks (RNNs) for MLC. ICSO overcomes feature selection issues and minimizes noise and redundancy in imbalanced datasets, whereas RNNs learn label dependencies to achieve higher accuracy. It is initially normalized using the Z-score method and then analyzed for dimensionality reduction using principal component analysis (PCA). Our approach is more accurate, more precise, better at recall, and achieves higher F-measure and specificity, and a lower error rate than current methods such as multi-label k-nearest neighbors (ML-KNN), fuzzy rough set learning with label-specific features (FRS-LIFT), and adaptive synthetic data for multi-label classification (ASD-MLC). The paper shows that ICSO is useful for augmenting RNN-based multi-label classification and may be applied in medical diagnosis and bioinformatics.