Exploring Correlation between Labels to improve Multi-Label Classification

Exploring Correlation between Labels to improve Multi-Label Classification

This paper attempts multi-label classification by extending the idea of independent binary classification models for each output label, and exploring how the inherent correlation between output labels can be used to improve predictions. Logistic Regression, Naive Bayes, Random Forest, and SVM models...

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Hauptverfasser: Garg, Amit, Noyola, Jonathan, Verma, Romil, Saxena, Ashutosh, Jami, Aditya
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Computer Science - Learning
Computer Science - Social and Information Networks
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Noyola, Jonathan
Verma, Romil
Saxena, Ashutosh
Jami, Aditya
description This paper attempts multi-label classification by extending the idea of independent binary classification models for each output label, and exploring how the inherent correlation between output labels can be used to improve predictions. Logistic Regression, Naive Bayes, Random Forest, and SVM models were constructed, with SVM giving the best results: an improvement of 12.9\% over binary models was achieved for hold out cross validation by augmenting with pairwise correlation probabilities of the labels.
doi_str_mv 10.48550/arxiv.1511.07953
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Computer Science - Social and Information Networks
title Exploring Correlation between Labels to improve Multi-Label Classification
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