|Table of Contents|

An Empirical Comparison of Label Detection Techniques for Multi-Label Classification(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2012年04期
Page:
55-61
Research Field:
Publishing date:

Info

Title:
An Empirical Comparison of Label Detection Techniques for Multi-Label Classification
Author(s):
Liu Jiali; Xu Jianhua
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
multi-label classification; k-nearest neighbor algorithm;  linear regression threshold function; multi-output linear regression;  logistic regression; discrete Bayesian rule
PACS:
TP18
DOI:
-
Abstract:
Now some multi-label classification methods cascade two different classification techniques in essence． The former is to build a label ranking system， and the latter to detect relevant labels effectively and improve classification performance further． To compare the different detection techniques，we collect four general label detection approaches: linear regression threshold，multiple output linear regression， logistic regression and discrete Bayesian methods． With k-nearest neighbor algorithm as a baseline method，we conduct an extensive experimental comparison on ten benchmark data sets． Our experimental results demonstrate that multiple output linear regression technique is recommendable，according to both computational time and classification performance．

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Last Update: 2013-03-21