mlsanityData to 14 Oct 2026

Random Feature Pruning for Multi-Class Classification

arXiv 2610.15101 ↗First posted 10 Oct 2026cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Weighted sums of random kitchen sinks (W-RKS) is a commonly used feature extraction technique for supervised learning tasks. A major drawback of this method is the redundancy and ineffectiveness of many extracted random features. This paper investigates efficient methods for reducing such redundancy in multi-class classification problems. We specifically formulate feature selection as a variant of the sequentially nested model selection problem and introduce the Successive Empirical Excess Risk Difference Maximization (SEED-MAX) algorithm. It recursively computes empirical risks to identify the most parsimonious yet informative set of features. We derive an analytical lower bound for the probability of correct feature selection by the SEED-MAX algorithm. This lower bound on the probability of correct feature selection can be made arbitrarily close to one by increasing the training dataset size. To further enhance the rate of pruning, we evaluate the features retained by SEED-MAX using the Fisher test (F-Test). Empirical evaluations on UCR time-series classification datasets show that the sequential application of SEED-MAX with the Fisher test achieves approximately 92 percent rate of pruning with negligible degradation in classification accuracy. Implementations of the SEED-MAX and SEED-MAX-F-Test methods are available at https://github.com/OmidiAmin/seed-max-ftest.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 53 references that are in the map. First posted on arXiv 10 Oct 2026.

Builds on

22 of 53 references.

References · 22

  1. 2018The UCR Time Series Archivecited by 431
  2. 2019ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelscited by 339
  3. 2020MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classificationcited by 190
  4. 2016Generalization Properties of Learning with Random Featurescited by 164
  5. 2015On the Equivalence between Kernel Quadrature Rules and Random Feature Expansionscited by 138
  6. 2020Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyondcited by 109
  7. 2021MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationcited by 90
  8. 2022HYDRA: Competing convolutional kernels for fast and accurate time series classificationcited by 54
  9. 2018On the Spectrum of Random Features Maps of High Dimensional Datacited by 28
  10. 2022HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computingcited by 14
  11. 2022Concentration of Random Feature Matrices in High-Dimensionscited by 9
  12. 2023Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelscited by 8
10 more
  1. 2022S-Rocket: Selective Random Convolution Kernels for Time Series Classificationcited by 7
  2. 2022Overparameterized random feature regression with nearly orthogonal datacited by 7
  3. 2023POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspectivecited by 5
  4. 2025Regularized Random Fourier Features and Finite Element Reconstruction for Operator Learning in Sobolev Spacecited by 2
  5. 2026Scalable Random Wavelet Features: Efficient Non-Stationary Kernel Approximation with Convergence Guaranteescited by 2
  6. 2014Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II)cited by 1
  7. 2014Multiple Output Regression with Latent Noisecited by 1
  8. 2024Model Selection Through Model Sortingcited by 1
  9. 2025HIT-ROCKET: Hadamard-vector Inner-product Transformer for ROCKETcited by 1
  10. 2026Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operatorscited by 1

Built on by

Not cited yet.