I want to know how multi-label classification work in detail. At the same time, how does the confusion matrix for multi-label classification is constructed? How does the performance measures such as accuracy, precision, recall, and f-measure is calculated for multi-label classification?
Dear Rama Ranjan Panda ,
these below links may be helpful to you to find the answer
https://www.researchgate.net/post/How_can_I_draw_ROC_Curve_Matlab_in_case_of_multiclass_classification_for_Face_Recognition_task
https://www.researchgate.net/post/How_to_calculate_Precision_and_Recall
https://www.researchgate.net/post/How_do_you_measure_specificity_and_sensitivity_in_a_multiple_class_classification_problem
Conference Paper Multi-label Classification of Clinical Text Documents consid...
https://monkeylearn.com/text-classification/
https://towardsdatascience.com/journey-to-the-center-of-multi-label-classification-384c40229bff
https://stackabuse.com/python-for-nlp-multi-label-text-classification-with-keras/
For multi label or multi class classification please read: https://scikit-learn.org/stable/modules/multiclass.html and a book: http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf
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