关键词: 中医临床数据, 多标记学习, 粒计算, 权重

Abstract: WML-kNN (weighted multi-label [k] nearest neighbor) learning algorithm, the number of neighbor points from fixed value, without considering the actual characteristics of the sample data, may make the high similarity point excluded from the neighbor set, or the low similarity point contained in the neighbor set, which will affect the performance of classifier. Traditional Chinese medicine (TCM) clinical data on the disease are likely to have multiple labels, and because of the particularity of the sample, each sample may have different similarity neighbors. This paper improves the WML-kNN algorithm and proposes WML-GkNN (WML-granular kNN) algorithm. In WML-GkNN algorithm, the granular control is used to control the granularity space, and the set of neighbors is determined, so that the sample points in the neighborhood have high similarity. The experimental results on the meridian resistance data collected by TCM show that the WML-GkNN algorithm improves the classification performance.

Key words: Chinese medicine clinical data, multi-label learning, granular computing, weight