Ensembles of Multi-instance Learners
ECML 2003, LNAI 2837, pp. 492-502, 2003.，-0001，（）：
In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. Through analyzing two famous multi-instance learning algorithms, this paper shows that many supervised learning algorithms can be adapted to multi-instance learning, as long as their focuses are shifted from the discrimination on the instances to the discrimination on the bags. Moreover, considering that ensemble learning paradigms can eﬀectively enhance supervised learners, this paper proposes to build ensembles of multi-instance learners to solve multi-instance problems. Experiments on a real-world benchmark test show that ensemble learning paradigms can signiﬁcantly enhance multi-instance learners, and the result achieved by EM-DD ensemble exceeds the best result on the benchmark test reported in literature.
版权说明：以下全部内容由周志华上传于 2005年08月02日 17时44分24秒，版权归本人所有。