MIML library: A modular and flexible library for multi-instance multi-label learning

被引：2

作者：

Belmonte, Alvaro ^{[1
]}

Zafra, Amelia ^{[1
,2
]}

Gibaja, Eva ^{[1
,2
,3
]}

机构：

[1] Univ Cordoba, Dept Comp Sci & Numer Anal, Cordoba, Spain

[2] Univ Cordoba, Andalusian Res Inst Data Sci & Computat Intelligen, Cordoba, Spain

[3] Univ Cordoba, Dept Comp Sci & Numer Anal, Campus Rabanales,Albert Einstein Bldg, 3rd floor, Cordoba 14071, Spain

来源：

NEUROCOMPUTING | 2022年 / 500卷

关键词：

Multi-instance learning; Multi-label learning; Weka; Mulan; Classification; CLASSIFICATION;

D O I：

10.1016/j.neucom.2022.05.068

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

MIML library is a Java software tool to develop, test, and compare classification algorithms for multiinstance multi-label (MIML) learning. The library includes 43 algorithms and provides a specific format and facilities for data managing and partitioning, holdout and cross-validation methods, standard metrics for performance evaluation, and generation of reports. In addition, algorithms can be executed through xml configuration files without needing to program. It is platform-independent, extensible, free, opensource, and available on GitHub under the GNU General Public License.(c) 2022 Elsevier B.V. All rights reserved.

引用

页码：632 / 636

页数：5

共 50 条

[1] Multi-instance multi-label learning
Zhou, Zhi-Hua
Zhang, Min-Ling
Huang, Sheng-Jun
Li, Yu-Feng
ARTIFICIAL INTELLIGENCE, 2012, 176 (01) : 2291 - 2320
[2] Instance Annotation for Multi-Instance Multi-Label Learning
Briggs, Forrest
Fern, Xiaoli Z.
Raich, Raviv
Lou, Qi
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2013, 7 (03)
[3] Learnability of multi-instance multi-label learning
Wang Wei
Zhou ZhiHua
CHINESE SCIENCE BULLETIN, 2012, 57 (19): : 2488 - 2491
[4] Learnability of multi-instance multi-label learning
WANG Wei & ZHOU ZhiHua National Key Laboratory for Novel Software Technology
Chinese Science Bulletin, 2012, 57 (19) : 2492 - 2495
[5] Multi-Instance Multi-Label Active Learning
Huang, Sheng-Jun
Gao, Nengneng
Chen, Songcan
PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2017, : 1886 - 1892
[6] Fast Multi-Instance Multi-Label Learning
Huang, Sheng-Jun
Gao, Wei
Zhou, Zhi-Hua
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2019, 41 (11) : 2614 - 2627
[7] Active Multi-Instance Multi-Label Learning
Retz, Robert
Schwenker, Friedhelm
ANALYSIS OF LARGE AND COMPLEX DATA, 2016, : 91 - 101
[8] Fast Multi-Instance Multi-Label Learning
Huang, Sheng-Jun
Gao, Wei
Zhou, Zhi-Hua
PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2014, : 1868 - 1874
[9] MIML-AI: MIXED-SUPERVISION MULTI-INSTANCE MULTI-LABEL LEARNING WITH AUXILIARY INFORMATION
Tam Nguyen
Raich, Raviv
Fern, Xiaoli Z.
Pham, Anh T.
2017 IEEE 27TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2017,
[10] Markov-MIML: A Markov chain-based multi-instance multi-label learning algorithm
Wu, Qingyao
Ng, Michael K.
Ye, Yunming
KNOWLEDGE AND INFORMATION SYSTEMS, 2013, 37 (01) : 83 - 104

← 1 2 3 4 5 →