A Novel Under-sampling Method with Gaussian Mixture and Jensen-Shannon Divergence

Due to bias towards major class samples, the traditional classifier cannot obtain excellent performance in imbalanced data. The under-sampling approach is one of the effective methods in imbalance classification. Nowadays, most of the under-sampling methods do not consider the global and local distribution of samples and the intra-class imbalance problem simultaneously. In addition, the sampling quantity cannot be adjusted adaptively in the practical application. To handle the above problems, in this paper, we propose a novel under-sampling approach with the Gaussian mixture model and Jensen-Shannon divergence, called GD-US. Firstly, it utilizes a Gaussian mixture model to cluster the majority class samples. And then, it calculates the number of sampling in each cluster according to its sparsity. In addition, GD-US adopts the global and local distribution of samples to decide the probability of deleting samples. Eventually, the Jensen-Shannon divergence is used to control the number of sampling. The experimental results show that the proposal can help solve the imbalance problem and enhance the classification accuracy.

• Publication year

  2022

• Venue

  International Conference on Image, Video and Signal Processing

• Publication date

  2022-03-18

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1145/3531232.3531262
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Generative Adversarial Networks and Markov Random Fields for oversampling very small training sets

  2021cited by this paper
• Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE

  2019cited by this paper
• Learning From Imbalanced Data

  2019cited by this paper
• Oversampling for Imbalanced Learning Based on K-Means and SMOTE

  2017cited by this paper
• GIR-based ensemble sampling approaches for imbalanced learning

  2017cited by this paper
• Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

  2017cited by this paper
• An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

  2016cited by this paper
• Under-sampling technique based on data density distribution

  2016cited by this paper
• A new one-class SVM based on hidden information

  2014cited by this paper
• Classifying peer-to-peer applications using imbalanced concept-adapting very fast decision tree on IP data stream

  2013cited by this paper
• Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning

  2005cited by this paper
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models

  2003cited by this paper
• A new look at the statistical model identification

  1974cited by this paper
• Knowledge-Based Systems

  year unknowncited by this paper

• Nonequilibrium chemical short-range order in metallic alloys

  2024cites this paper