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文件名称:Multi-Label Ensemble Learning
文件大小:365KB
文件格式:PDF
更新时间:2019-03-21 17:06:56
Multi-Label Ensemble Learning
Multi-label learning aims at predicting potentially multiple
labels for a given instance. Conventional multi-label learning approaches
focus on exploiting the label correlations to improve the accuracy of
the learner by building an individual multi-label learner or a combined
learner based upon a group of single-label learners. However, the gener-
alization ability of such individual learner can be weak. It is well known
that ensemble learning can effectively improve the generalization abil-
ity of learning systems by constructing multiple base learners and the
performance of an ensemble is related to the both accuracy and diver-
sity of base learners. In this paper, we study the problem of multi-label
ensemble learning. Specifically, we aim at improving the generalization
ability of multi-label learning systems by constructing a group of multi-
label base learners which are both accurate and diverse. We propose
a novel solution, called EnML, to effectively augment the accuracy as
well as the diversity of multi-label base learners. In detail, we design
two objective functions to evaluate the accuracy and diversity of multi-
label base learners, respectively, and EnML simultaneously optimizes
these two objectives with an evolutionary multi-objective optimization
method. Experiments on real-world multi-label learning tasks validate
the effectiveness of our approach against other well-established methods.