计算机科学 ›› 2017, Vol. 44 ›› Issue (12): 48-51.doi: 10.11896/j.issn.1002-137X.2017.12.009
环天,郝宁,牛强
HUAN Tian, HAO Ning and NIU Qiang
摘要： 针对多示例多标记学习算法MIMLSVM只从包层面构造聚类,而忽略了包内示例分布对分类造成影响这一不足,提出一种基于概念权重向量的MIMLSVM改进算法——I-MIMLSVM算法。首先从示例层面构造聚类,挖掘出示例中的潜在概念簇,运用R-PATTERN算法计算每个概念簇的概念权重；然后利用TF-IDF算法计算每个概念簇在各个示例包中的重要度；最后将示例包表示为概念权重向量,向量的每一维即为概念簇的概念权重与其在该包中的重要度的乘积。将该算法在包含2000幅图像的自然数据集上进行实验验证,结果表明改进的算法在分类性能上整体优于原算法,尤其在Hamming loss,Coverage和Average precision这3个测评指标上较为明显。
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