TP311
国家自然科学基金资助项目（61262040）。
应用特征选择处理多标签数据分类时"维度灾难"问题已成为重要研究方向，因此提出一种基于邻域维护准则的特征选择算法（NPFS，feature selection algorithm based on neighborhood preservation criterion）。通过近似基于特征子空间和基于标签空间的2个相似度矩阵来构建相似性维护表达式，再通过线性近似扩展相似性维护公式得到邻域关系维护公式，并计算出邻域关系维护得分（NRPS，neighborhood relationship preserving score）来评估特征子集的重要性，结合贪婪方法设计具有NRPS的多标签特征选择算法（NPFS）。仿真结果表明，对比MMIFS算法和MDMR算法，所提出的算法在平均准确率、覆盖率、汉明损失、1-错误率、排名损失5个性能指标上均有改善。
The application of feature selection to deal with "Dimensional Disaster" in multi-label data classification has become an important research direction, we proposed a feature selection algorithm based on neighborhood preservation criterion (NPFS). A similarity preservation expression was constructed by approximating two similarity matrices based on feature subspace and label space. Then, the similarity preservation formulation was extended by the linear approximation to obtain a formulation of the neighborhood relationship preservation, and the importance of the feature subset was evaluated by calculating the neighborhood relationship preserving score (NRPS).A multi-label feature selection algorithm with NRPS was designed in combination with the greedy method(NPFS).The simulation results show that the metrics of average precision, coverage, hamming loss, one-error, ranking loss obtained by the proposed algorithm have been improved compared with those obtained by MMIFS algorithm and MDMR algorithm.
刘云,肖雪.基于邻域维护准则的特征选择算法优化研究[J].重庆大学学报,2019,42(3):58-64.
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