【摘要】 多标签分类算法已广泛应用于文本分类、图像识别、基因功能分类等领域,为了解决多标签分类算法较少考量标签之间的相关性等问题,提出一种基于标签相关性的多标签分类算法。首先对BP神经网络进行改进以适应多标签分类算法,然后对标签集分别使用皮尔逊相关系数和关联规则进行二阶、高阶相关性分析,最后将标签的相关性与改进的BP神经网络算法得到的概率做线性插值,得到样本属于某标签的最终概率。通过在4个真实数据集上利用5个多标签分类指标做对比实验,验证了提出的算法分类效果明显优于现有的多标签分类算法。更多还原

【Abstract】 Multi-label classification algorithm has been widely used in text classification, image recognition,gene functional classification and other fields. In order to solve the problem that multi-label classification algorithm seldom considers the correlation between labels, this paper proposes a multi-label classification algorithm based on label correlation. Firstly, the BP neural network was improved to adapt to the multi-label classification algorithm.Then we respectively used Pearson correlation coefficient and association rules to analyze the second-order and highorder correlation of the label set. Finally, we linearly interpolated the correlation between the two label and the multiple label with the improved BP neural network algorithm to obtain the final probability that this sample belonged to some label. Based on the comparison of five multi-label classification indicators on four real data sets, it is verified that the proposed algorithm is better than the existing multi-label classification algorithm.更多还原