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They define transformation methods as ap- proaches that transform multi-label classification into one or more single-label classification. Authors define ...
In this paper, we propose a novel problem transformation that leverage label dependency. We used Reuters-21578 corpus that is among the most used for text ...
Multi-label classification methods can be broadly classified asProblem transformation and Algorithm adaptation. This paper presents anoverview of single-label ...
Nov 1, 2024 · Multi-label classification (MLC) is a type of supervised machine learning that allows instances to be categorized into two or more labels ...
In this study, a novel MLC approach is introduced called Multi-Label Classification with Label Clusters (MLC–LC), which incorporates label correlations into a ...
Multi-Label Classification Using Problem Transformation Approach and Machine Learning on Text Mining for Multiple Event Detection · 3 Citations · 19 References.
In this paper an Anomaly Detection based Multi Label Classification using Association Rule Mining (ADMLCAR) is used for solving MLC problem.
Oct 22, 2024 · This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative ...
This survey provides an overview of multi-label learning and its algorithms, including problem transformation and algorithm adaptation. We also introduced ...
Jun 7, 2018 · There are two main methods for tackling a multi-label classification problem: problem transformation methods and algorithm adaptation methods.