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
收藏
页码：632 / 636
页数：5
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