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A flexible model-free prediction-based framework for feature ranking.

Jingyi Jessica Li¹, Yiling Elaine Chen¹, Xin Tong²

¹Department of Statistics, University of California, Los Angeles.

Journal of Machine Learning Research : JMLR

|March 24, 2022

Summary

This summary is machine-generated.

We introduce new methods for marginal feature ranking in binary classification, addressing limitations of current approaches. Our criteria, Classical Criterion (CC) and Neyman-Pearson Criterion (NPC), improve accuracy and handle sampling biases common in biomedical research.

Keywords:

binary classification classical and Neyman-Pearson paradigms marginal feature ranking model-free sampling bias

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Area of Science:

* Statistical modeling and machine learning
* Computational biology and bioinformatics
* Data science and predictive analytics

Background:

* Scientists often analyze features individually (marginally) despite advanced joint modeling tools, prioritizing interpretability and visualization.
* Existing marginal ranking methods like Pearson correlation, t-test, and Wilcoxon rank-sum test inadequately consider feature distributions and prediction goals.
* Marginal feature ranking is crucial for scientific discovery, particularly in identifying cancer driver genes.

Purpose of the Study:

* To propose novel marginal ranking criteria for binary classification that account for feature distributions and prediction objectives.
* To develop model-free, nonparametric implementations of these criteria for broad applicability.
* To address the limitations of traditional ranking methods in scientific research.

Main Methods:

* Proposed two new ranking criteria: Classical Criterion (CC) and Neyman-Pearson Criterion (NPC).
* Utilized model-free, nonparametric approaches to accommodate diverse feature distributions.
* Provided theoretical analysis demonstrating sample-level ranking consistency with population-level counterparts.
* Investigated the robustness of NPC to sampling bias.

Main Results:

* Both CC and NPC demonstrated high probability of consistent sample-level ranking with population-level counterparts.
* NPC showed robustness to deviations in class proportions between sample and population, crucial for biomedical data.
* Simulations and real-data studies validated the effectiveness and advantages of CC and NPC.

Conclusions:

* The proposed CC and NPC offer improved marginal feature ranking for binary classification.
* NPC's robustness to sampling bias makes it particularly valuable for biomedical applications.
* The objective-based, model-free ranking approach is extendable to feature subsets and other prediction tasks.