Fast nonparametric feature selection with error control using integrated path stability selection

• URL: http://arxiv.org/abs/2410.02208v1
• Date: Thu, 3 Oct 2024 04:42:28 GMT
• Title: Fast nonparametric feature selection with error control using integrated path stability selection
• Authors: Omar Melikechi, David B. Dunson, Jeffrey W. Miller,
• Abstract summary: We introduce a general feature selection method that applies integrated path stability selection to thresholding to control false positives and the false discovery rate. We focus on two special cases of the general method based on gradient boosting (IPSSGB) and random forests (IPSSRF) Extensive simulations with RNA sequencing data show that IPSSGB and IPSSRF have better error control, detect more true positives, and are faster than existing methods.
• Score: 12.608885112539202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature selection can greatly improve performance and interpretability in machine learning problems. However, existing nonparametric feature selection methods either lack theoretical error control or fail to accurately control errors in practice. Many methods are also slow, especially in high dimensions. In this paper, we introduce a general feature selection method that applies integrated path stability selection to thresholding to control false positives and the false discovery rate. The method also estimates q-values, which are better suited to high-dimensional data than p-values. We focus on two special cases of the general method based on gradient boosting (IPSSGB) and random forests (IPSSRF). Extensive simulations with RNA sequencing data show that IPSSGB and IPSSRF have better error control, detect more true positives, and are faster than existing methods. We also use both methods to detect microRNAs and genes related to ovarian cancer, finding that they make better predictions with fewer features than other methods.

Related papers

• Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes [54.18828236350544]
  Propensity score matching (PSM) addresses selection biases by selecting comparable populations for analysis. Different matching methods can produce significantly different Average Treatment Effects (ATE) for the same task, even when meeting all validation criteria. To address this issue, we introduce a novel metric, A2A, to reduce the number of valid matches.
  arXiv  Detail & Related papers  (2024-07-20T12:42:24Z)
• Integrated path stability selection [5.263910852465186]
  We introduce a novel approach to stability selection based on integrating stability paths rather than maximizing over them. This yields upper bounds on E(FP) that are orders of magnitude stronger than previous bounds, leading to significantly more true positives in practice for the same target E(FP) We demonstrate the method on simulations and real data from prostate and colon cancer studies.
  arXiv  Detail & Related papers  (2024-03-23T15:55:52Z)
• Stochastic Optimal Control Matching [53.156277491861985]
  Our work introduces Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for optimal control. The control is learned via a least squares problem by trying to fit a matching vector field. Experimentally, our algorithm achieves lower error than all the existing IDO techniques for optimal control.
  arXiv  Detail & Related papers  (2023-12-04T16:49:43Z)
• Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials [2.048226951354646]
  We show how to select the adjustment approach -- which variables and in which form -- to maximize precision. Our approach maintains Type-I error control (under the null) and offers substantial gains in precision. When applied to real data, we also see meaningful efficiency improvements overall and within subgroups.
  arXiv  Detail & Related papers  (2022-10-31T16:25:38Z)
• Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm [101.44676036551537]
  One-way Partial AUC (OPAUC) and Two-way Partial AUC (TPAUC) measures the average performance of a binary classifier. Most of the existing methods could only optimize PAUC approximately, leading to inevitable biases that are not controllable. We present a simpler reformulation of the PAUC problem via distributional robust optimization AUC.
  arXiv  Detail & Related papers  (2022-10-08T08:26:22Z)
• Directional FDR Control for Sub-Gaussian Sparse GLMs [4.229179009157074]
  False discovery rate (FDR) control aims to identify some small number of statistically significantly nonzero results. We construct the debiased matrix-Lasso estimator and prove the normality by minimax-rate oracle inequalities for sparse GLMs.
  arXiv  Detail & Related papers  (2021-05-02T05:34:32Z)
• Learning Sampling Policy for Faster Derivative Free Optimization [100.27518340593284]
  We propose a new reinforcement learning based ZO algorithm (ZO-RL) with learning the sampling policy for generating the perturbations in ZO optimization instead of using random sampling. Our results show that our ZO-RL algorithm can effectively reduce the variances of ZO gradient by learning a sampling policy, and converge faster than existing ZO algorithms in different scenarios.
  arXiv  Detail & Related papers  (2021-04-09T14:50:59Z)
• Sparse PCA via $l_{2,p}$-Norm Regularization for Unsupervised Feature Selection [138.97647716793333]
  We propose a simple and efficient unsupervised feature selection method, by combining reconstruction error with $l_2,p$-norm regularization. We present an efficient optimization algorithm to solve the proposed unsupervised model, and analyse the convergence and computational complexity of the algorithm theoretically.
  arXiv  Detail & Related papers  (2020-12-29T04:08:38Z)
• Robust Multi-class Feature Selection via $l_{2,0}$-Norm Regularization Minimization [6.41804410246642]
  Feature selection is an important computational-processing in data mining and machine learning. In this paper, a novel method based on homoy hard threshold (HIHT) is proposed to solve the least square problem for multi-class feature selection.
  arXiv  Detail & Related papers  (2020-10-08T02:06:06Z)
• Implicit differentiation of Lasso-type models for hyperparameter optimization [82.73138686390514]
  We introduce an efficient implicit differentiation algorithm, without matrix inversion, tailored for Lasso-type problems. Our approach scales to high-dimensional data by leveraging the sparsity of the solutions.
  arXiv  Detail & Related papers  (2020-02-20T18:43:42Z)
• Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic [5.395127324484869]
  SplitSGD is a new dynamic learning schedule for optimization. The method decreases the learning rate for better adaptation to the local geometry of the objective function. It essentially does not incur additional computational cost than standard SGD.
  arXiv  Detail & Related papers  (2019-10-18T19:38:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.