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Reseach Article

Study and Analysis of Multi-Label Classification Methods in Data Mining

by Shubhangi R. Khade, Suraj R. Balwan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 159 - Number 9
Year of Publication: 2017
Authors: Shubhangi R. Khade, Suraj R. Balwan
10.5120/ijca2017913035

Shubhangi R. Khade, Suraj R. Balwan . Study and Analysis of Multi-Label Classification Methods in Data Mining. International Journal of Computer Applications. 159, 9 ( Feb 2017), 9-12. DOI=10.5120/ijca2017913035

@article{ 10.5120/ijca2017913035,
author = { Shubhangi R. Khade, Suraj R. Balwan },
title = { Study and Analysis of Multi-Label Classification Methods in Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2017 },
volume = { 159 },
number = { 9 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = { 9-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume159/number9/27028-2017913035/ },
doi = { 10.5120/ijca2017913035 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:05:18.773750+05:30
%A Shubhangi R. Khade
%A Suraj R. Balwan
%T Study and Analysis of Multi-Label Classification Methods in Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 159
%N 9
%P 9-12
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Multi-label classification is major research problem in machine learning domain. Multi-label classification is nothing but the variants of classification problem in which different target labels should be allocated to every instance. Multi-label classification is different from the multiclass classification. In general, multi-label classification is defined as problem of searching model which maps the input to binary vectors, rather than outputs in scalars. Basically there are two different techniques for handling the multi-label classification problem such as techniques of problem transformation and techniques of algorithm adaptation. In problem transformation approaches, multi-label classification problem is transformed to binary classification problems set and this can be further processed through single class classifiers. In algorithm adaptation approaches, algorithms are adapted in order to perform the multi-label classification directly. In this paper, different multi-label classification algorithms are studied and evaluated with current research problems. Methods such as binary relevance (BR), high-order approaches, hierarchical tree based algorithms, and the most recent method called ML-Forest are studied and evaluated with different real time datasets such as medical, emotions, yeast etc.

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Index Terms

Computer Science
Information Sciences

Keywords

BR HOMER TSA ML-Forest Multi-label classification datasets accuracy