It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data,especially for those with very small number of samples.Feature selection especial...It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data,especially for those with very small number of samples.Feature selection especially the unsupervised ones are the right way to deal with this challenge and realize the task.Therefore,two unsupervised spectral feature selection algorithms are proposed in this paper.They group features using advanced Self-Tuning spectral clustering algorithm based on local standard deviation,so as to detect the global optimal feature clusters as far as possible.Then two feature ranking techniques,including cosine-similarity-based feature ranking and entropy-based feature ranking,are proposed,so that the representative feature of each cluster can be detected to comprise the feature subset on which the explainable classification system will be built.The effectiveness of the proposed algorithms is tested on high dimensional benchmark omics datasets and compared to peer methods,and the statistical test are conducted to determine whether or not the proposed spectral feature selection algorithms are significantly different from those of the peer methods.The extensive experiments demonstrate the proposed unsupervised spectral feature selection algorithms outperform the peer ones in comparison,especially the one based on cosine similarity feature ranking technique.The statistical test results show that the entropy feature ranking based spectral feature selection algorithm performs best.The detected features demonstrate strong discriminative capabilities in downstream classifiers for omics data,such that the AI system built on them would be reliable and explainable.It is especially significant in building transparent and trustworthy medical diagnostic systems from an interpretable AI perspective.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant Nos.62076159,12031010,61673251,and 61771297)was also supported by the Fundamental Research Funds for the Central Universities(GK202105003)+1 种基金the Natural Science Basic Research Program of Shaanxi Province of China(2022JM334)the Innovation Funds of Graduate Programs at Shaanxi Normal University(2015CXS028 and 2016CSY009).
文摘It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data,especially for those with very small number of samples.Feature selection especially the unsupervised ones are the right way to deal with this challenge and realize the task.Therefore,two unsupervised spectral feature selection algorithms are proposed in this paper.They group features using advanced Self-Tuning spectral clustering algorithm based on local standard deviation,so as to detect the global optimal feature clusters as far as possible.Then two feature ranking techniques,including cosine-similarity-based feature ranking and entropy-based feature ranking,are proposed,so that the representative feature of each cluster can be detected to comprise the feature subset on which the explainable classification system will be built.The effectiveness of the proposed algorithms is tested on high dimensional benchmark omics datasets and compared to peer methods,and the statistical test are conducted to determine whether or not the proposed spectral feature selection algorithms are significantly different from those of the peer methods.The extensive experiments demonstrate the proposed unsupervised spectral feature selection algorithms outperform the peer ones in comparison,especially the one based on cosine similarity feature ranking technique.The statistical test results show that the entropy feature ranking based spectral feature selection algorithm performs best.The detected features demonstrate strong discriminative capabilities in downstream classifiers for omics data,such that the AI system built on them would be reliable and explainable.It is especially significant in building transparent and trustworthy medical diagnostic systems from an interpretable AI perspective.
