MEKA: A Multi-label/Multi-target Extension to WEKA

被引：0
|
作者：
Read, Jesse [1 ,2 ]
Reutemann, Peter [3 ]
Pfahringer, Bernhard [3 ]
Holmes, Geoff [3 ]
机构：
[1] Aalto Univ, Espoo, Finland
[2] Aalto Univ, Dept Comp Sci, Espoo, Finland
[3] Univ Waikato, Dept Comp Sci, Hamilton, New Zealand
来源：
JOURNAL OF MACHINE LEARNING RESEARCH | 2016年 / 17卷
关键词：
classification; learning; multi-label; multi-target; incremental;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present MEKA : an open-source Java framework based on the well-known WEKA library. MEKA provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi-label experiments and development. It supports multi-label and multi-target data, including in incremental and semi-supervised contexts.
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页数：5
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