LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection

• URL: http://arxiv.org/abs/2011.00215v1
• Date: Sat, 31 Oct 2020 08:50:28 GMT
• Title: LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection
• Authors: Shuyin Xia, Wenhua Li, Guoyin Wang, Xinbo Gao, Changqing Zhang, Elisabeth Giem
• Abstract summary: We propose the LRA framework for accelerating rough set algorithms. It is a general-purpose framework which can be applied to almost all rough set methods significantly.
• Score: 81.19294803707648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose and prove the theorem regarding the stability of attributes in a decision system. Based on the theorem, we propose the LRA framework for accelerating rough set algorithms. It is a general-purpose framework which can be applied to almost all rough set methods significantly . Theoretical analysis guarantees high efficiency. Note that the enhancement of efficiency will not lead to any decrease of the classification accuracy. Besides, we provide a simpler prove for the positive approximation acceleration framework.

Related papers

• Parameter-Free Algorithms for Performative Regret Minimization under Decision-Dependent Distributions [15.396561118589577]
  performative risk minimization is a formulation of optimization under decision-dependent distributions. Our algorithms significantly improve upon existing Lipschitz constant distribution parameter-based methods. We provide experimental results that demonstrate the numerical superiority of our algorithms over the existing method and other black-box optimistic optimization methods.
  arXiv  Detail & Related papers  (2024-02-23T08:36:28Z)
• A Novel Framework for Improving the Breakdown Point of Robust Regression Algorithms [1.9594639581421422]
  We present an effective framework for improving the breakdown point of robust regression algorithms. We derive a consistent robust regression algorithm with iterative local search (CORALS)
  arXiv  Detail & Related papers  (2023-05-20T15:59:33Z)
• Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability [107.65337427333064]
  optimization of the Area Under the Precision-Recall Curve (AUPRC) is a crucial problem for machine learning. In this work, we present the first trial in the single-dependent generalization of AUPRC optimization. Experiments on three image retrieval datasets on speak to the effectiveness and soundness of our framework.
  arXiv  Detail & Related papers  (2022-09-27T09:06:37Z)
• Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application [49.66088514485446]
  Best-Response Constraint (BRC) is a general learning framework to explicitly formulate the potential dependency of the generator on the discriminator. We show that even with different motivations and formulations, a variety of existing GANs ALL can be uniformly improved by our flexible BRC methodology.
  arXiv  Detail & Related papers  (2022-05-20T12:42:41Z)
• Towards Extremely Fast Bilevel Optimization with Self-governed Convergence Guarantees [42.514612465664605]
  We propose a single-level formulation to uniformly understand existing explicit and implicit Gradient-based BLOs. A striking feature of our convergence result is that, compared to those original unaccelerated GBLO versions, the fast BAGDC admits a unified non-asymptotic convergence theory towards stationarity.
  arXiv  Detail & Related papers  (2022-05-20T09:46:10Z)
• False Correlation Reduction for Offline Reinforcement Learning [115.11954432080749]
  We propose falSe COrrelation REduction (SCORE) for offline RL, a practically effective and theoretically provable algorithm. We empirically show that SCORE achieves the SoTA performance with 3.1x acceleration on various tasks in a standard benchmark (D4RL)
  arXiv  Detail & Related papers  (2021-10-24T15:34:03Z)
• Selective Classification via One-Sided Prediction [54.05407231648068]
  One-sided prediction (OSP) based relaxation yields an SC scheme that attains near-optimal coverage in the practically relevant high target accuracy regime. We theoretically derive bounds generalization for SC and OSP, and empirically we show that our scheme strongly outperforms state of the art methods in coverage at small error levels.
  arXiv  Detail & Related papers  (2020-10-15T16:14:27Z)
• Target-Embedding Autoencoders for Supervised Representation Learning [111.07204912245841]
  This paper analyzes a framework for improving generalization in a purely supervised setting, where the target space is high-dimensional. We motivate and formalize the general framework of target-embedding autoencoders (TEA) for supervised prediction, learning intermediate latent representations jointly optimized to be both predictable from features as well as predictive of targets.
  arXiv  Detail & Related papers  (2020-01-23T02:37:10Z)

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.