Jiang Jiefang, Zhang Xianyong, Yang Jilin
School of Mathematical Sciences, Sichuan Normal University, Chengdu, 610066 China.
Institute of Intelligent Information and Quantum Information, Sichuan Normal University, Chengdu, 610066 China.
Artif Intell Rev. 2023;56(5):4077-4112. doi: 10.1007/s10462-022-10274-6. Epub 2022 Sep 19.
Feature selection facilitates intelligent information processing, and the unsupervised learning of feature selection has become important. In terms of unsupervised feature selection, the Laplacian score (LS) provides a powerful measurement and optimization method, and good performance has been achieved using the recent forward iterative Laplacian score (FILS) algorithm. However, there is still room for advancement. The aim of this paper is to improve the FILS algorithm, and thus, feature significance (SIG) is mainly introduced to develop a high-quality selection method, i.e., the incremental forward iterative Laplacian score (IFILS) algorithm. Based on the modified LS, the metric difference in the incremental feature process motivates SIG. Therefore, SIG offers a dynamic characterization by considering initial and terminal states, and it promotes the current FILS measurement on only the terminal state. Then, both the modified LS and integrated SIG acquire granulation nonmonotonicity and uncertainty, especially on incremental feature chains, and the corresponding verification is achieved by completing examples and experiments. Furthermore, a SIG-based incremental criterion of minimum selection is designed to choose optimization features, and thus, the IFILS algorithm is naturally formulated to implement unsupervised feature selection. Finally, an in-depth comparison of the IFILS algorithm with the FILS algorithm is achieved using data experiments on multiple datasets, including a nominal dataset of COVID-19 surveillance. As validated by the experimental results, the IFILS algorithm outperforms the FILS algorithm and achieves better classification performance.
特征选择有助于智能信息处理，无监督特征选择学习变得至关重要。在无监督特征选择方面，拉普拉斯分数（LS）提供了一种强大的度量和优化方法，并且使用最近的前向迭代拉普拉斯分数（FILS）算法已取得了良好的性能。然而，仍有改进的空间。本文的目的是改进FILS算法，因此，主要引入特征显著性（SIG）来开发一种高质量的选择方法，即增量前向迭代拉普拉斯分数（IFILS）算法。基于改进的LS，增量特征过程中的度量差异激发了SIG。因此，SIG通过考虑初始状态和终端状态提供了一种动态表征，并且它仅促进当前对终端状态的FILS度量。然后，改进的LS和整合的SIG都具有粒度非单调性和不确定性，特别是在增量特征链上，并且通过完整的示例和实验实现了相应的验证。此外，设计了基于SIG的最小选择增量准则来选择优化特征，从而自然地制定了IFILS算法以实现无监督特征选择。最后，使用包括COVID-19监测名义数据集在内的多个数据集上的数据实验，对IFILS算法和FILS算法进行了深入比较。实验结果验证了IFILS算法优于FILS算法，并实现了更好的分类性能。
