Label dependency modeling in Multi-Label Naïve Bayes through input space expansion
<p dir="ltr">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 e...
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2024
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| _version_ | 1864513543143948288 |
|---|---|
| author | PKA Chitra (21749216) |
| author2 | Saravana Balaji Balasubramanian (21749219) Omar Khattab (21749222) Omar Al Kadri (21152969) |
| author2_role | author author author |
| author_facet | PKA Chitra (21749216) Saravana Balaji Balasubramanian (21749219) Omar Khattab (21749222) Omar Al Kadri (21152969) |
| author_role | author |
| dc.creator.none.fl_str_mv | PKA Chitra (21749216) Saravana Balaji Balasubramanian (21749219) Omar Khattab (21749222) Omar Al Kadri (21152969) |
| dc.date.none.fl_str_mv | 2024-12-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.7717/peerj-cs.2093 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Label_dependency_modeling_in_Multi-Label_Na_ve_Bayes_through_input_space_expansion/29605490 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science Machine learning Software engineering Mathematical sciences Statistics Multi-label Naïve Bayesian classification Label dependency Input space expansion Heterogeneous feature space Mixed joint density distribution |
| dc.title.none.fl_str_mv | Label dependency modeling in Multi-Label Naïve Bayes through input space expansion |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: PeerJ Computer Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.7717/peerj-cs.2093" target="_blank">https://dx.doi.org/10.7717/peerj-cs.2093</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_bacce55112f3d218645b1b82b034095c |
| identifier_str_mv | 10.7717/peerj-cs.2093 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29605490 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Label dependency modeling in Multi-Label Naïve Bayes through input space expansionPKA Chitra (21749216)Saravana Balaji Balasubramanian (21749219)Omar Khattab (21749222)Omar Al Kadri (21152969)Information and computing sciencesArtificial intelligenceData management and data scienceMachine learningSoftware engineeringMathematical sciencesStatisticsMulti-label Naïve Bayesian classificationLabel dependencyInput space expansionHeterogeneous feature spaceMixed joint density distribution<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: PeerJ Computer Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.7717/peerj-cs.2093" target="_blank">https://dx.doi.org/10.7717/peerj-cs.2093</a></p>2024-12-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.7717/peerj-cs.2093https://figshare.com/articles/journal_contribution/Label_dependency_modeling_in_Multi-Label_Na_ve_Bayes_through_input_space_expansion/29605490CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296054902024-12-01T00:00:00Z |
| spellingShingle | Label dependency modeling in Multi-Label Naïve Bayes through input space expansion PKA Chitra (21749216) Information and computing sciences Artificial intelligence Data management and data science Machine learning Software engineering Mathematical sciences Statistics Multi-label Naïve Bayesian classification Label dependency Input space expansion Heterogeneous feature space Mixed joint density distribution |
| status_str | publishedVersion |
| title | Label dependency modeling in Multi-Label Naïve Bayes through input space expansion |
| title_full | Label dependency modeling in Multi-Label Naïve Bayes through input space expansion |
| title_fullStr | Label dependency modeling in Multi-Label Naïve Bayes through input space expansion |
| title_full_unstemmed | Label dependency modeling in Multi-Label Naïve Bayes through input space expansion |
| title_short | Label dependency modeling in Multi-Label Naïve Bayes through input space expansion |
| title_sort | Label dependency modeling in Multi-Label Naïve Bayes through input space expansion |
| topic | Information and computing sciences Artificial intelligence Data management and data science Machine learning Software engineering Mathematical sciences Statistics Multi-label Naïve Bayesian classification Label dependency Input space expansion Heterogeneous feature space Mixed joint density distribution |