Label dependency modeling in Multi-Label Naive Bayes through input space expansion.

ABSTRACT: In the realm of multi-label learning, instances are often characterized by a plurality of labels, diverging from the single-label paradigm prevalent in conventional datasets. Multi-label techniques often employ a similar feature space to build classification models for every label. Nevertheless, labels typically convey distinct semantic information and should possess their own unique attributes. Several approaches have been suggested to identify label-specific characteristics for creating distinct categorization models. Our proposed methodology seeks to encapsulate and systematically represent label correlations within the learning framework. The innovation of improved multi-label Naïve Bayes (iMLNB) lies in its strategic expansion of the input space, which assimilates meta informat

SUBMITTER: Chitra P 

PROVIDER: S-EPMC11784536 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature