Granular correlation-based label-specific feature augmentation for multi-label classification

Multi-label classification is an extension of single-label classification with generations of multi-output for unseen instances. Label correlation is an essential component in constructing multi-label classifiers. How to optimize the representation of label correlation while preserving the semantics of label-specific remains an uncertain issue. Instead of estimating label correlation by a holistic feature representation, we present an augmented label correlation model by generating multi-granularity label-specific features. Firstly, we devise a mixture distance measure to characterize the closeness of an instance by weighing the Pearson correlation coefficient with cosine similarity. Secondly, we explore the local label-specific relative discrimination by leveraging from both the instance-level and class-level correlation distribution within k nearest neighborhood. Finally, we conduct an information fusion strategy to comprehensively integrate the positive and the negative tendencies at the neighborhood level. Instances with salient positive tendency and compact neighborhood structure receive larger values while receiving smaller values with salient negative tendency and sparse neighborhood structure. With the concatenation of original features and augmented features, we examine the classification performance of the proposed granule correlation-based feature augmentation (GOFA) on well-established second-order multi-label classification methods. Extensive comparisons on thirteen benchmarks demonstrate the statistical superiority of GOFA over state-of-the-art multi-label classifications.