MVMR-FS : Non-parametric feature selection algorithm based on Maximum inter-class Variation and Minimum Redundancy

• URL: http://arxiv.org/abs/2307.14643v1
• Date: Thu, 27 Jul 2023 06:33:17 GMT
• Title: MVMR-FS : Non-parametric feature selection algorithm based on Maximum inter-class Variation and Minimum Redundancy
• Authors: Haitao Nie, Shengbo Zhang, Bin Xie
• Abstract summary: We propose a non-parametric feature selection algorithm based on maximum inter-class variation and minimum redundancy. Compared with ten state-of-the-art methods, MVMR-FS achieves the highest average accuracy and improves the accuracy by 5% to 11%.
• Score: 1.2522889958051284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to accurately measure the relevance and redundancy of features is an age-old challenge in the field of feature selection. However, existing filter-based feature selection methods cannot directly measure redundancy for continuous data. In addition, most methods rely on manually specifying the number of features, which may introduce errors in the absence of expert knowledge. In this paper, we propose a non-parametric feature selection algorithm based on maximum inter-class variation and minimum redundancy, abbreviated as MVMR-FS. We first introduce supervised and unsupervised kernel density estimation on the features to capture their similarities and differences in inter-class and overall distributions. Subsequently, we present the criteria for maximum inter-class variation and minimum redundancy (MVMR), wherein the inter-class probability distributions are employed to reflect feature relevance and the distances between overall probability distributions are used to quantify redundancy. Finally, we employ an AGA to search for the feature subset that minimizes the MVMR. Compared with ten state-of-the-art methods, MVMR-FS achieves the highest average accuracy and improves the accuracy by 5% to 11%.

Related papers

• Fast Model Selection and Stable Optimization for Softmax-Gated Multinomial-Logistic Mixture of Experts Models [40.216463162163976]
  We develop a batch minorization-maximization algorithm for softmax-gated multinomial-logistic MoE.<n>We also prove finite-sample rates for conditional density estimation and parameter recovery.<n>Experiments on biological protein--protein interaction prediction validate the full pipeline.
  arXiv  Detail & Related papers  (2026-02-08T14:45:41Z)
• MS-ISSM: Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity [65.85858856481131]
  unstructured and irregular nature of point clouds poses a significant challenge for objective quality assessment (PCQA)<n>We propose the Multi-scale Implicit Structural Similarity Measurement (MS-ISSM)
  arXiv  Detail & Related papers  (2026-01-03T14:58:52Z)
• Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets [2.6037922505725675]
  There isn't a universally optimal feature selection method applicable to any data distribution. This paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works.
  arXiv  Detail & Related papers  (2024-07-22T13:08:50Z)
• Learning Algorithms for Verification of Markov Decision Processes [20.5951492453299]
  We present a general framework for applying learning algorithms to the verification of Markov decision processes (MDPs) The presented framework focuses on probabilistic reachability, which is a core problem in verification.
  arXiv  Detail & Related papers  (2024-03-14T08:54:19Z)
• Feature Selection via Maximizing Distances between Class Conditional Distributions [9.596923373834093]
  We propose a novel feature selection framework based on the distance between class conditional distributions, measured by integral probability metrics (IPMs) Our framework directly explores the discriminative information of features in the sense of distributions for supervised classification. Experimental results show that our framework can outperform state-of-the-art methods in terms of classification accuracy and robustness to perturbations.
  arXiv  Detail & Related papers  (2024-01-15T06:10:10Z)
• Anomaly Detection Under Uncertainty Using Distributionally Robust Optimization Approach [0.9217021281095907]
  Anomaly detection is defined as the problem of finding data points that do not follow the patterns of the majority. The one-class Support Vector Machines (SVM) method aims to find a decision boundary to distinguish between normal data points and anomalies. A distributionally robust chance-constrained model is proposed in which the probability of misclassification is low.
  arXiv  Detail & Related papers  (2023-12-03T06:13:22Z)
• A Targeted Accuracy Diagnostic for Variational Approximations [8.969208467611896]
  Variational Inference (VI) is an attractive alternative to Markov Chain Monte Carlo (MCMC) Existing methods characterize the quality of the whole variational distribution. We propose the TArgeted Diagnostic for Distribution Approximation Accuracy (TADDAA)
  arXiv  Detail & Related papers  (2023-02-24T02:50:18Z)
• Optimization of Annealed Importance Sampling Hyperparameters [77.34726150561087]
  Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. We present a parameteric AIS process with flexible intermediary distributions and optimize the bridging distributions to use fewer number of steps for sampling. We assess the performance of our optimized AIS for marginal likelihood estimation of deep generative models and compare it to other estimators.
  arXiv  Detail & Related papers  (2022-09-27T07:58:25Z)
• Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty [58.144520501201995]
  Bi-Lipschitz regularization of neural network layers preserve relative distances between data instances in the feature spaces of each layer. With the use of an attentive set encoder, we propose to meta learn either diagonal or diagonal plus low-rank factors to efficiently construct task specific covariance matrices. We also propose an inference procedure which utilizes scaled energy to achieve a final predictive distribution.
  arXiv  Detail & Related papers  (2021-10-12T22:04:19Z)
• Hybrid Random Features [60.116392415715275]
  We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) HRFs automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest.
  arXiv  Detail & Related papers  (2021-10-08T20:22:59Z)
• Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation [99.92568326314667]
  We propose the amortized conditional normalized maximum likelihood (ACNML) method as a scalable general-purpose approach for uncertainty estimation. Our algorithm builds on the conditional normalized maximum likelihood (CNML) coding scheme, which has minimax optimal properties according to the minimum description length principle. We demonstrate that ACNML compares favorably to a number of prior techniques for uncertainty estimation in terms of calibration on out-of-distribution inputs.
  arXiv  Detail & Related papers  (2020-11-05T08:04:34Z)
• A Discriminative Technique for Multiple-Source Adaptation [55.5865665284915]
  We present a new discriminative technique for the multiple-source adaptation, MSA, problem. Our solution only requires conditional probabilities that can easily be accurately estimated from unlabeled data from the source domains. Our experiments with real-world applications further demonstrate that our new discriminative MSA algorithm outperforms the previous generative solution.
  arXiv  Detail & Related papers  (2020-08-25T14:06:15Z)
• Rethink Maximum Mean Discrepancy for Domain Adaptation [77.2560592127872]
  This paper theoretically proves two essential facts: 1) minimizing the Maximum Mean Discrepancy equals to maximize the source and target intra-class distances respectively but jointly minimize their variance with some implicit weights, so that the feature discriminability degrades. Experiments on several benchmark datasets not only prove the validity of theoretical results but also demonstrate that our approach could perform better than the comparative state-of-art methods substantially.
  arXiv  Detail & Related papers  (2020-07-01T18:25:10Z)

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.