MMAO-Cls: Metabolic Multi-Agent Optimization for Joint Feature Selection and Classifier Tuning

• URL: http://arxiv.org/abs/2607.01539v2
• Date: Sat, 04 Jul 2026 15:42:32 GMT
• Title: MMAO-Cls: Metabolic Multi-Agent Optimization for Joint Feature Selection and Classifier Tuning
• Abstract summary: Multi-Agent Metabolic (MMAO) can act as a credible outer-loop for classification model selection.<n>We propose MMAO-Cls, a mixed-space realization in which each agent jointly encodes a binary feature mask and hyperparameters.<n>We evaluate MMAO-Cls on seven standard benchmarks with three seeds each and compare it against RandomSearch, GA-lite, PSO-lite, and an endogenous no-sharing ablation.
• Score: 0.5156484100374058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies whether the Metabolic Multi-Agent Optimizer (MMAO) can act as a credible outer-loop optimizer for classification model selection. We propose MMAO-Cls, a mixed-space realization in which each agent jointly encodes a binary feature mask and classifier hyperparameters, while private energy, communal budget, role drift, and lifecycle turnover are mapped to the accuracy-complexity tradeoff of wrapper learning. The implementation is strengthened by deriving feature-budget adaptation from feature-information priors and by regularizing validation reward with both subset compactness and train-validation overfitting gap. We evaluate MMAO-Cls on seven standard tabular benchmarks with three seeds each and compare it against RandomSearch, GA-lite, PSO-lite, and an endogenous no-sharing ablation. On the aggregate validation objective, MMAO-Cls ranks second ($0.9433$) behind GA-lite ($0.9446$). On held-out test performance, it reaches mean score $0.8882$, improving over RandomSearch ($0.8808$) and GA-lite ($0.8857$), remaining close to PSO-lite ($0.8874$) and the no-sharing ablation ($0.8900$), while using the most compact mean held-out feature subset among all compared methods (feature ratio $0.4881$). Pairwise tests show that these margins are not yet statistically significant. The resulting claim is therefore conservative: MMAO-Cls supports classification applicability and compact mixed-space search more clearly than it isolates communal sharing as a decisive standalone advantage.

Related papers

• The Sample Complexity of Multiclass and Sparse Contextual Bandits [106.74652380822778]
  We aim to identify an approximately optimal policy from a given class based on bandit feedback.<n>Motivated by bandit multiclass classification with zero-one rewards, we focus on the emph$s$-sparse setting.<n>We show that, with $s$-sparse rewards, the induced model class admits a sharp DEC bound that scales with $s$ and directly yields the optimal rate.
  arXiv  Detail & Related papers  (2026-05-28T09:12:20Z)
• Decoupled Conformal Optimisation: Efficient Prediction Sets via Independent Tuning and Calibration [20.21455697379946]
  We propose Decoupled Conformal optimisation (DCO) as a train-tune-calibrate design principle.<n>DCO uses an independent tuning split for efficiency-oriented structural selection and a fresh calibration split for the final conformal quantile.<n>We show that DCO tracks the nominal coverage level closely while often reducing average prediction-set size or interval width relative to PAC-style calibration.
  arXiv  Detail & Related papers  (2026-05-18T13:10:21Z)
• Direct Bethe Free Energy Minimization for Bayesian Neural Network [0.0]
  We propose training Bayesian neural networks by directly minimizing the Bethe free energy rather than maximizing a variational lower bound.<n>On tree-structured factor graphs the Bethe free energy is exact; deterministic layers drop out of the objective and are trained by standard backpropagation.<n>All variants admit a closed-form predictive at MAP-equivalent inference cost, in contrast to ensemble and sampling-based methods.
  arXiv  Detail & Related papers  (2026-05-08T20:12:48Z)
• High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking [69.90407799170687]
  We study high-probability (HP) convergence guarantees in decentralized optimization.<n>Our results establish the same conditions on the horizon as in achieving comparable times.
  arXiv  Detail & Related papers  (2026-04-30T22:45:21Z)
• Reinforcement Learning from Multi-Source Imperfect Preferences: Best-of-Both-Regimes Regret [71.69884486156359]
  We study episodic RL from emphmulti-source imperfect preferences through a cumulative imperfection budget.<n>We propose a unified algorithm with regret $tildeO(sqrtK/M+)$, which exhibits a best-of-both-regimes behavior.
  arXiv  Detail & Related papers  (2026-03-20T19:34:53Z)
• Almost Asymptotically Optimal Active Clustering Through Pairwise Observations [59.20614082241528]
  We propose a new analysis framework for clustering $M$ items into an unknown number of $K$ distinct groups using noisy and actively collected responses.<n>We establish a fundamental lower bound on the expected number of queries needed to achieve a desired confidence in the accuracy of the clustering.<n>We develop a computationally feasible variant of the Generalized Likelihood Ratio statistic and show that its performance gap to the lower bound can be accurately empirically estimated.
  arXiv  Detail & Related papers  (2026-02-05T14:16:47Z)
• Enhancing Unsupervised Feature Selection via Double Sparsity Constrained Optimization [6.342485512772862]
  Unsupervised single feature selection (UFS) is widely applied in machine learning and pattern recognition.<n>Most of the existing methods only consider sparsity, which makes it difficult to select subsets and discriminative from the original.<n>In this paper, we propose a new method called DSCOFS to consider a subset and discriminative from the original.
  arXiv  Detail & Related papers  (2025-01-01T05:05:46Z)
• SPLITZ: Certifiable Robustness via Split Lipschitz Randomized Smoothing [8.471466670802817]
  SPLITZ is a practical and novel approach to provide certifiable robustness to adversarial examples.<n> Motivation for SPLITZ comes from the observation that many standard deep networks exhibit heterogeneity in Lipschitz constants.<n>We show that SPLITZ consistently improves on existing state-of-the-art approaches in the MNIST, CIFAR-10 and ImageNet datasets.
  arXiv  Detail & Related papers  (2024-07-03T05:13:28Z)
• Combinatorial Stochastic-Greedy Bandit [79.1700188160944]
  We propose a novelgreedy bandit (SGB) algorithm for multi-armed bandit problems when no extra information other than the joint reward of the selected set of $n$ arms at each time $tin [T]$ is observed. SGB adopts an optimized-explore-then-commit approach and is specifically designed for scenarios with a large set of base arms.
  arXiv  Detail & Related papers  (2023-12-13T11:08:25Z)
• FABind: Fast and Accurate Protein-Ligand Binding [127.7790493202716]
  $mathbfFABind$ is an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. Our proposed model demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods.
  arXiv  Detail & Related papers  (2023-10-10T16:39:47Z)
• Contextual Combinatorial Bandits with Probabilistically Triggered Arms [55.9237004478033]
  We study contextual bandits with probabilistically triggered arms (C$2$MAB-T) under a variety of smoothness conditions. Under the triggering modulated (TPM) condition, we devise the C$2$-UC-T algorithm and derive a regret bound $tildeO(dsqrtT)$.
  arXiv  Detail & Related papers  (2023-03-30T02:51:00Z)
• The Interplay between Distribution Parameters and the Accuracy-Robustness Tradeoff in Classification [0.0]
  Adrial training tends to result in models that are less accurate on natural (unperturbed) examples compared to standard models. This can be attributed to either an algorithmic shortcoming or a fundamental property of the training data distribution. In this work, we focus on the latter case under a binary Gaussian mixture classification problem.
  arXiv  Detail & Related papers  (2021-07-01T06:57:50Z)

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.