A novel embedded min-max approach for feature selection in nonlinear support vector machine classification

• URL: http://arxiv.org/abs/2004.09863v4
• Date: Fri, 15 Jan 2021 15:40:42 GMT
• Title: A novel embedded min-max approach for feature selection in nonlinear support vector machine classification
• Authors: Asunci\'on Jim\'enez-Cordero, Juan Miguel Morales and Salvador Pineda
• Abstract summary: We propose an embedded feature selection method based on a min-max optimization problem. By leveraging duality theory, we equivalently reformulate the min-max problem and solve it without further ado. The efficiency and usefulness of our approach are tested on several benchmark data sets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In recent years, feature selection has become a challenging problem in several machine learning fields, such as classification problems. Support Vector Machine (SVM) is a well-known technique applied in classification tasks. Various methodologies have been proposed in the literature to select the most relevant features in SVM. Unfortunately, all of them either deal with the feature selection problem in the linear classification setting or propose ad-hoc approaches that are difficult to implement in practice. In contrast, we propose an embedded feature selection method based on a min-max optimization problem, where a trade-off between model complexity and classification accuracy is sought. By leveraging duality theory, we equivalently reformulate the min-max problem and solve it without further ado using off-the-shelf software for nonlinear optimization. The efficiency and usefulness of our approach are tested on several benchmark data sets in terms of accuracy, number of selected features and interpretability.

Related papers

• An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
  We first construct a max-margin optimization-based model to model potentially non-monotonic preferences. We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration. Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
  arXiv  Detail & Related papers  (2024-09-04T14:36:20Z)
• Multi-class Support Vector Machine with Maximizing Minimum Margin [67.51047882637688]
  Support Vector Machine (SVM) is a prominent machine learning technique widely applied in pattern recognition tasks. We propose a novel method for multi-class SVM that incorporates pairwise class loss considerations and maximizes the minimum margin. Empirical evaluations demonstrate the effectiveness and superiority of our proposed method over existing multi-classification methods.
  arXiv  Detail & Related papers  (2023-12-11T18:09:55Z)
• Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification [0.0]
  We present novel Twin Parametric Margin Support Vector Machine (TPMSVM) models to tackle the problem of multiclass classification. We construct bounded-by-norm uncertainty sets around each sample and derive the robust counterpart of deterministic models. We test the proposed TPMSVM methodology on real-world datasets, showing the good performance of the approach.
  arXiv  Detail & Related papers  (2023-06-09T19:27:24Z)
• Bilevel Optimization for Feature Selection in the Data-Driven Newsvendor Problem [8.281391209717105]
  We study the feature-based news vendor problem, in which a decision-maker has access to historical data. In this setting, we investigate feature selection, aiming to derive sparse, explainable models with improved out-of-sample performance. We present a mixed integer linear program reformulation for the bilevel program, which can be solved to optimality with standard optimization solvers.
  arXiv  Detail & Related papers  (2022-09-12T08:52:26Z)
• Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization [73.17488635491262]
  Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. This article introduces EBCS-SVM: evolutionary bilevel cost-sensitive SVMs.
  arXiv  Detail & Related papers  (2022-04-21T16:08:44Z)
• Feature Selection Using Reinforcement Learning [0.0]
  The space of variables or features that can be used to characterize a particular predictor of interest continues to grow exponentially. Identifying the most characterizing features that minimizes the variance without jeopardizing the bias of our models is critical to successfully training a machine learning model.
  arXiv  Detail & Related papers  (2021-01-23T09:24:37Z)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• Feature Selection for Huge Data via Minipatch Learning [0.0]
  We propose Stable Minipatch Selection (STAMPS) and Adaptive STAMPS. STAMPS are meta-algorithms that build ensembles of selection events of base feature selectors trained on tiny, (ly-adaptive) random subsets of both the observations and features of the data. Our approaches are general and can be employed with a variety of existing feature selection strategies and machine learning techniques.
  arXiv  Detail & Related papers  (2020-10-16T17:41:08Z)
• 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)
• Saliency-based Weighted Multi-label Linear Discriminant Analysis [101.12909759844946]
  We propose a new variant of Linear Discriminant Analysis (LDA) to solve multi-label classification tasks. The proposed method is based on a probabilistic model for defining the weights of individual samples. The Saliency-based weighted Multi-label LDA approach is shown to lead to performance improvements in various multi-label classification problems.
  arXiv  Detail & Related papers  (2020-04-08T19:40:53Z)
• Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
  We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
  arXiv  Detail & Related papers  (2020-01-29T09:40:58Z)

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.