Label dependency modeling in Multi-Label Naïve Bayes through input space expansion

• Published in: PeerJ. Computer science Vol. 10; p. e2093
• Main Authors: Chitra, PKA, Balasubramanian, Saravana Balaji, Khattab, Omar, Al-Kadri, Mhd Omar
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 10.12.2024; PeerJ Inc
• Subjects: Algorithms; Analysis; Heterogeneous feature space; Input space expansion; Label dependency; Mixed joint density distribution; Multi-label Naïve Bayesian classification; Heterogeneous feature space; Input space expansion; Label dependency; Mixed joint density distribution; Multi-label Naïve Bayesian classification
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.2093

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 information derived from the label space, thereby engendering a composite input domain that encompasses both continuous and categorical variables. To accommodate the heterogeneity of the expanded input space, we refine the likelihood parameters of iMLNB using a joint density function, which is adept at handling the amalgamation of data types. We subject our enhanced iMLNB model to a rigorous empirical evaluation, utilizing six benchmark datasets. The performance of our approach is gauged against the traditional multi-label Naïve Bayes ( MLNB ) algorithm and is quantified through a suite of evaluation metrics. The empirical results not only affirm the competitive edge of our proposed method over the conventional MLNB but also demonstrate its superiority across the aforementioned metrics. This underscores the efficacy of modeling label dependencies in multi-label learning environments and positions our approach as a significant contribution to the field.