A novel multigranularity feature-selection method based on neighborhood mutual information and its application in autistic patient identification
A novel multigranularity feature-selection method based on neighborhood mutual information and its application in autistic patient identification
复制标题
DOI：
10.1016/j.bspc.2022.103887
复制
发表时间：
2022-09-01
影响因子：
5.1
通讯作者：
Zhang，Jiacai
中科院分区：
文献类型：
--
作者：
Shi，Chunlei;Xin，Xianwei;Zhang，Jiacai
高维、小样本的功能磁共振成像(FMRI)数据是机器学习应用于从fMRI图像中识别精神障碍的一大挑战。特征选择为提取任务相关的fMRI特征和去除冗余特征提供了一种有效的方法。已有的特征选择方法是在候选特征之间弱相关性的限制下，通过选择可区分的特征来提高机器学习模型的性能。然而，功能磁共振成像特征之间的强相关性及其对分类性能的实际影响被考虑得较少。为此，提出了一种新的多粒度特征选择方法，该方法同时考虑了特征的区分性和特征间的相关性。首先，使用k-均值聚类将fMRI样本划分为亚组，降低了亚组内潜在的异质性。其次，使用一种与特征相关性成正比、与区分度成反比的新权值来生成代表fMRI特征空间的最小生成树。第三，结合多粒度信息，从乐观和悲观两个角度进一步考察了特征之间的相关性对分类的影响。在ABASE数据库的fMRI数据上的实验结果表明，该方法不仅减少了特征冗余度，而且在识别自闭症谱系障碍(ASD)时优于各种竞争的特征选择方法。
The high dimensionality and small sample of functional magnetic resonance imaging (fMRI) data is the big challenge for machine learning application in identification of mental disorders from fMRI images. Feature selection provides an effective method to select the task related fMRI features and removing the redundant ones. The existing feature-selection methods improved the performance of machine learning model by selecting the discriminative features under the limitation of weak correlation among candidate features. However, the strong correlation among fMRI features and its actual influence on classification performance is less considered. Herein, a novel multigranularity feature-selection method was proposed, which considers both the feature’s discrimination and the correlation between features at the same time. Firstly, k-means clustering was used to divide fMRI samples into subgroup reducing the potential heterogeneity within subgroups. Second, a new weight proportional to features’ correlation and inversely proportional to the discrimination was used to create minimum spanning trees representing the fMRI feature space. Third, the impact of the correlation among features on the classification was further examined from optimistic and pessimistic perspectives with multigranularity information. The experimental results on fMRI data from ABIDE database show that our method not only reduced the feature redundancy but also is superior to a variety of competing feature-selection methods in autism spectrum disorder (ASD) recognition.