Multi-label classification by analyzing labels dependencies

Lena Tenenboim; Lior Rokach; Bracha Shapira

Multi-label classification by analyzing labels dependencies

Lena Tenenboim
, Lior Rokach
, Bracha Shapira

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This research study deals with the improvement of multi-label classification by modeling existing dependencies between labels. The main purpose of
the study is to define and develop a classification algorithm for multi-label classification tasks by partitioning the class set into several subsets. According to this
algorithm, first, dependencies among the labels are analyzed and then the whole
set of labels is divided into several mutually exclusive subsets. Finally, a classification algorithm incorporating dependencies among labels within each subset
can be applied. An experimental study shows that the proposed method has high
potential to achieve the defined objectives and improve multi-label classification
performance.

Original language	English
Title of host publication	Proceedings of the 1st international workshop on learning from multi-label data, Bled, Slovenia
Editors	Grigorios Tsoumakas, Min-Ling Zhang, Zhi-Hua Zhou
Place of Publication	Bled, Slovenia
Pages	117-132
Number of pages	16
State	Published - Aug 2009

Cite this

@inproceedings{8e97b737814c4337b607cf58d580d5fe,

title = "Multi-label classification by analyzing labels dependencies",

abstract = "This research study deals with the improvement of multi-label classification by modeling existing dependencies between labels. The main purpose of the study is to define and develop a classification algorithm for multi-label classification tasks by partitioning the class set into several subsets. According to this algorithm, first, dependencies among the labels are analyzed and then the whole set of labels is divided into several mutually exclusive subsets. Finally, a classification algorithm incorporating dependencies among labels within each subset can be applied. An experimental study shows that the proposed method has high potential to achieve the defined objectives and improve multi-label classification performance.",

author = "Lena Tenenboim and Lior Rokach and Bracha Shapira",

year = "2009",

month = aug,

language = "English",

pages = "117--132",

editor = "Tsoumakas, \{ Grigorios\} and Min-Ling Zhang and Zhi-Hua Zhou",

booktitle = "Proceedings of the 1st international workshop on learning from multi-label data, Bled, Slovenia",

}

Multi-label classification by analyzing labels dependencies. / Tenenboim, Lena; Rokach, Lior ; Shapira, Bracha.
Proceedings of the 1st international workshop on learning from multi-label data, Bled, Slovenia. ed. / Grigorios Tsoumakas; Min-Ling Zhang; Zhi-Hua Zhou. Bled, Slovenia, 2009. p. 117-132.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Rokach, Lior

AU - Shapira, Bracha

PY - 2009/8

Y1 - 2009/8

N2 - This research study deals with the improvement of multi-label classification by modeling existing dependencies between labels. The main purpose of the study is to define and develop a classification algorithm for multi-label classification tasks by partitioning the class set into several subsets. According to this algorithm, first, dependencies among the labels are analyzed and then the whole set of labels is divided into several mutually exclusive subsets. Finally, a classification algorithm incorporating dependencies among labels within each subset can be applied. An experimental study shows that the proposed method has high potential to achieve the defined objectives and improve multi-label classification performance.

AB - This research study deals with the improvement of multi-label classification by modeling existing dependencies between labels. The main purpose of the study is to define and develop a classification algorithm for multi-label classification tasks by partitioning the class set into several subsets. According to this algorithm, first, dependencies among the labels are analyzed and then the whole set of labels is divided into several mutually exclusive subsets. Finally, a classification algorithm incorporating dependencies among labels within each subset can be applied. An experimental study shows that the proposed method has high potential to achieve the defined objectives and improve multi-label classification performance.

M3 - Conference contribution

SP - 117

EP - 132

BT - Proceedings of the 1st international workshop on learning from multi-label data, Bled, Slovenia

A2 - Tsoumakas, Grigorios

A2 - Zhang, Min-Ling

A2 - Zhou, Zhi-Hua

CY - Bled, Slovenia

ER -

Multi-label classification by analyzing labels dependencies

Abstract

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