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

被引：3

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

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