Boosting-based Multi-label Classification

被引：0

作者：

Kajdanowicz, Tomasz ^{[1
]}

Kazienko, Przemyslaw ^{[1
]}

机构：

[1] Wroclaw Univ Technol, PL-50370 Wroclaw, Poland

来源：

JOURNAL OF UNIVERSAL COMPUTER SCIENCE | 2013年 / 19卷 / 04期

关键词：

multi-label classification; boosting; AdaBoostSeq; machine learning; PREDICTION;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Multi-label classification is a machine learning task that assumes that a data instance may be assigned with multiple number of class labels at the same time. Modelling of this problem has become an important research topic recently. This paper revokes AdaBoostSeq multi-label classification algorithm and examines it in order to check its robustness properties. It can be stated that AdaBoostSeq is able to result with quite stable Hamming Loss evaluation measure regardless of the size of input and output space.

引用

页码：502 / 520

页数：19

共 50 条

[1] Feature ranking for enhancing boosting-based multi-label text categorization
Al-Salemi, Bassam
Ayob, Masri
Noah, Shahrul Azman Mohd
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2018, 113 : 531 - 543
[2] Feature Selection based on Supervised Topic Modeling for Boosting-Based Multi-Label Text Categorization
Al-Salemi, Bassam
Ayob, Masri
Noah, Shahrul Azman Mohd
Ab Aziz, Mohd Juzaiddin
[J]. PROCEEDINGS OF THE 2017 6TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATICS (ICEEI'17), 2017,
[3] Model-Shared Subspace Boosting for Multi-label Classification
Yan, Rong
Tesic, Jelena
Smith, John R.
[J]. KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2007, : 834 - 843
[4] Multi-Label Classification Based on Associations
Alazaidah, Raed
Samara, Ghassan
Almatarneh, Sattam
Hassan, Mohammad
Aljaidi, Mohammad
Mansur, Hasan
[J]. APPLIED SCIENCES-BASEL, 2023, 13 (08):
[5] Minimum Variance Semi-Supervised Boosting for Multi-label Classification
Zhao, Chenyang
Zhai, Shaodan
[J]. 2015 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP), 2015, : 1342 - 1346
[6] Boosting Multi-Label Classification Performance Through Meta-Model
Guehria, Sonia
Belleili, Habiba
Azizi, Nabiha
Zenakhra, Djamel
[J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2024, 38 (01)
[7] A multi-label classification based approach for sentiment classification
Liu, Shuhua Monica
Chen, Jiun-Hung
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (03) : 1083 - 1093
[8] Multi-label Anomaly Classification Based on Electrocardiogram
Li, Chenyang
Sun, Le
[J]. HEALTH INFORMATION SCIENCE, HIS 2021, 2021, 13079 : 171 - 178
[9] Biclustering-based multi-label classification
Schmitke, Luiz Rafael
Paraiso, Emerson Cabrera
Nievola, Julio Cesar
[J]. KNOWLEDGE AND INFORMATION SYSTEMS, 2024, 66 (08) : 4861 - 4898
[10] Topic Model Based Multi-Label Classification
Padmanabhan, Divya
Bhat, Satyanath
Shevade, Shirish
Narahari, Y.
[J]. 2016 IEEE 28TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2016), 2016, : 996 - 1003

← 1 2 3 4 5 →