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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Veröffentlicht in:arXiv.org 2015-11
Hauptverfasser: Garg, Amit, Noyola, Jonathan, Verma, Romil, Saxena, Ashutosh, Aditya Jami
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Bayesian analysis
Classification
Correlation
Labels
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Noyola, Jonathan
Verma, Romil
Saxena, Ashutosh
Aditya Jami
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.
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subjects Bayesian analysis
Classification
Correlation
Labels
title Exploring Correlation between Labels to improve Multi-Label Classification
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