当前位置: X-MOL 学术Pattern Recogn. › 论文详情
Identifying the best data-driven feature selection method for boosting reproducibility in classification tasks
Pattern Recognition ( IF 5.898 ) Pub Date : 2020-01-09 , DOI: 10.1016/j.patcog.2019.107183
Nicolas Georges; Islem Mhiri; Islem Rekik; Alzheimer’s Disease Neuroimaging Initiative

Considering the proliferation of extremely high-dimensional data in many domains including computer vision and healthcare applications such as computer-aided diagnosis (CAD), advanced techniques for reducing the data dimensionality and identifying the most relevant features for a given classification task such as distinguishing between healthy and disordered brain states are needed. Despite the existence of many works on boosting the classification accuracy using a particular feature selection (FS) method, choosing the best one from a large pool of existing FS techniques for boosting feature reproducibility within a dataset of interest remains a formidable challenge to tackle. Notably, a good performance of a particular FS method does not necessarily imply that the experiment is reproducible and that the features identified are optimal for the entirety of the samples. Essentially, this paper presents the first attempt to address the following challenge: “Given a set of different feature selection methods {FS1,⋯,FSK}, and a dataset of interest, how to identify the most reproducible and ‘trustworthy’ connectomic features that would produce reliable biomarkers capable of accurately differentiate between two specific conditions?” To this aim, we propose FS-Select framework which explores the relationships among the different FS methods using a multi-graph architecture based on feature reproducibility power, average accuracy, and feature stability of each FS method. By extracting the ‘central’ graph node, we identify the most reliable and reproducible FS method for the target brain state classification task along with the most discriminative features fingerprinting these brain states. To evaluate the reproducibility power of FS-Select, we perturbed the training set by using different cross-validation strategies on a multi-view small-scale connectomic dataset (late mild cognitive impairment vs Alzheimer’s disease) and large-scale dataset including autistic vs healthy subjects. Our experiments revealed reproducible connectional features fingerprinting disordered brain states.
更新日期:2020-01-09

 

全部期刊列表>>
Springer Nature 2019高下载量文章和章节
化学/材料学中国作者研究精选
《科学报告》最新环境科学研究
ACS材料视界
自然科研论文编辑服务
中南大学国家杰青杨华明
剑桥大学-
中国科学院大学化学科学学院
材料化学和生物传感方向博士后招聘
课题组网站
X-MOL
北京大学分子工程苏南研究院
华东师范大学分子机器及功能材料
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug