Comparative evaluation of multi-label classification methods

This paper presents a comparative evaluation of popular multi-label classification methods on several multi-label problems from different domains. The methods include multi-label k-nearest neighbor, binary relevance, label power set, random k-label set ensemble learning, calibrated label ranking, hi...

Full description

Saved in:
Bibliographic Details
Published in2012 9th International Conference on Fuzzy Systems and Knowledge Discovery pp. 679 - 683
Main Authors Nasierding, G., Kouzani, A. Z.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2012
Subjects
algorithm
Algorithm design and analysis
Biomedical imaging
Classification algorithms
comparative evaluation
Conferences
Data mining
evaluation metrics
Measurement
multi-label classification
multi-label data
Prediction algorithms
Online AccessGet full text
ISBN9781467300254
146730025X
DOI10.1109/FSKD.2012.6234347

Cover

More Information
Summary:This paper presents a comparative evaluation of popular multi-label classification methods on several multi-label problems from different domains. The methods include multi-label k-nearest neighbor, binary relevance, label power set, random k-label set ensemble learning, calibrated label ranking, hierarchy of multi-label classifiers and triple random ensemble multi-label classification algorithms. These multi-label learning algorithms are evaluated using several widely used MLC evaluation metrics. The evaluation results show that for each multi-label classification problem a particular MLC method can be recommended. The multi-label evaluation datasets used in this study are related to scene images, multimedia video frames, diagnostic medical report, email messages, emotional music data, biological genes and multi-structural proteins categorization.
ISBN:9781467300254
146730025X
DOI:10.1109/FSKD.2012.6234347