ArticlePloS one2019
Performance comparison of linear and non-linear feature selection methods for the analysis of large survey datasets.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Advanced machine learning framework for thyroid cancer epidemiology in Iran through integration of environmental socioeconomic and health system predictors.Scientific reports · 2025Article
- Interpretable machine learning for precision cognitive aging.Frontiers in computational neuroscience · 2025Article
- A Subtype Perspective on Cognitive Trajectories in Healthy Aging.Brain sciences · 2024Article
- Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits.PloS one · 2024Article
- Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation.Bioengineering (Basel, Switzerland) · 2022Article
- Cuffless Blood Pressure Measurement Using Linear and Nonlinear Optimized Feature Selection.Diagnostics (Basel, Switzerland) · 2022Article
- A roadmap for multi-omics data integration using deep learning.Briefings in bioinformatics · 2022Review
- Robotic Kinematic measures of the arm in chronic Stroke: part 2 - strong correlation with clinical outcome measures.Bioelectronic medicine · 2021Article
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Authors and funding
5 authors.
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Abstract
Large survey databases for aging-related analysis are often examined to discover key factors that affect a dependent variable of interest. Typically, this analysis is performed with methods assuming linear dependencies between variables. Such assumptions however do not hold in many cases, wherein data are linked by way of non-linear dependencies. This in turn requires applications of analytic methods, which are more accurate in identifying potentially non-linear dependencies. Here, we objectively compared the feature selection performance of several frequently-used linear selection methods and three non-linear selection methods in the context of large survey data. These methods were assessed using both synthetic and real-world datasets, wherein relationships between the features and dependent variables were known in advance. In contrast to linear methods, we found that the non-linear methods offered better overall feature selection performance than linear methods in all usage conditions. Moreover, the performance of the non-linear methods was more stable, being unaffected by the inclusion or exclusion of variables from the datasets. These properties make non-linear feature selection methods a potentially preferable tool for both hypothesis-driven and exploratory analyses for aging-related datasets.
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