Feature Selection via Maximizing Distances between Class Conditional Distributions

• URL: http://arxiv.org/abs/2401.07488v1
• Date: Mon, 15 Jan 2024 06:10:10 GMT
• Title: Feature Selection via Maximizing Distances between Class Conditional Distributions
• Authors: Chunxu Cao, Qiang Zhang
• Abstract summary: 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.
• Score: 9.596923373834093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For many data-intensive tasks, feature selection is an important preprocessing step. However, most existing methods do not directly and intuitively explore the intrinsic discriminative information of features. 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. We analyze the theoretical and practical aspects of IPMs for feature selection, construct criteria based on IPMs. We propose several variant feature selection methods of our framework based on the 1-Wasserstein distance and implement them on real datasets from different domains. Experimental results show that our framework can outperform state-of-the-art methods in terms of classification accuracy and robustness to perturbations.

Related papers

• A data-science pipeline to enable the Interpretability of Many-Objective Feature Selection [0.1474723404975345]
  Many-Objective Feature Selection (MOFS) approaches use four or more objectives to determine the relevance of a subset of features in a supervised learning task. This paper proposes an original methodology to support data scientists in the interpretation and comparison of the MOFS outcome by combining post-processing and visualisation of the set of solutions.
  arXiv  Detail & Related papers  (2023-11-30T17:44:22Z)
• An Additive Instance-Wise Approach to Multi-class Model Interpretation [53.87578024052922]
  Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. Existing methods mainly focus on selecting explanatory input features, which follow either locally additive or instance-wise approaches. This work exploits the strengths of both methods and proposes a global framework for learning local explanations simultaneously for multiple target classes.
  arXiv  Detail & Related papers  (2022-07-07T06:50:27Z)
• Predicting Out-of-Domain Generalization with Neighborhood Invariance [59.05399533508682]
  We propose a measure of a classifier's output invariance in a local transformation neighborhood. Our measure is simple to calculate, does not depend on the test point's true label, and can be applied even in out-of-domain (OOD) settings. In experiments on benchmarks in image classification, sentiment analysis, and natural language inference, we demonstrate a strong and robust correlation between our measure and actual OOD generalization.
  arXiv  Detail & Related papers  (2022-07-05T14:55:16Z)
• Parallel feature selection based on the trace ratio criterion [4.30274561163157]
  This work presents a novel parallel feature selection approach for classification, namely Parallel Feature Selection using Trace criterion (PFST) Our method uses trace criterion, a measure of class separability used in Fisher's Discriminant Analysis, to evaluate feature usefulness. The experiments show that our method can produce a small set of features in a fraction of the amount of time by the other methods under comparison.
  arXiv  Detail & Related papers  (2022-03-03T10:50:33Z)
• Selecting the suitable resampling strategy for imbalanced data classification regarding dataset properties [62.997667081978825]
  In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class. This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples. Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class.
  arXiv  Detail & Related papers  (2021-12-15T18:56:39Z)
• Interpretable Multi-dataset Evaluation for Named Entity Recognition [110.64368106131062]
  We present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area.
  arXiv  Detail & Related papers  (2020-11-13T10:53:27Z)
• 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)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)
• Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification [91.67977602992657]
  We propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training.
  arXiv  Detail & Related papers  (2020-03-20T15:44:17Z)
• Outlier Detection Ensemble with Embedded Feature Selection [42.8338013000469]
  We propose an outlier detection ensemble framework with embedded feature selection (ODEFS) For each random sub-sampling based learning component, ODEFS unifies feature selection and outlier detection into a pairwise ranking formulation. We adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection.
  arXiv  Detail & Related papers  (2020-01-15T13:14:10Z)

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.