Binary Classification: Counterbalancing Class Imbalance by Applying Regression Models in Combination with One-Sided Label Shifts

@inproceedings{Bellmann2020BinaryCC,
  title={Binary Classification: Counterbalancing Class Imbalance by Applying Regression Models in Combination with One-Sided Label Shifts},
  author={Peter Bellmann and Heinke Hihn and Daniel A. Braun and Friedhelm Schwenker},
  booktitle={International Conference on Agents and Artificial Intelligence},
  year={2020}
}
In many real-world pattern recognition scenarios, such as in medical applications, the corresponding classification tasks can be of an imbalanced nature. In the current study, we focus on binary, imbalanced classification tasks, i.e.~binary classification tasks in which one of the two classes is under-represented (minority class) in comparison to the other class (majority class). In the literature, many different approaches have been proposed, such as under- or oversampling, to counter class… 
[PDF] Semantic Reader

Tables from this paper

References

SHOWING 1-10 OF 28 REFERENCES

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

A taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based is proposed and a thorough empirical comparison is developed by the consideration of the most significant published approaches to show whether any of them makes a difference.

Class-Boundary Alignment for Imbalanced Dataset Learning

The class-boundaryalignment algorithm is proposed to augment SVMs to deal with imbalanced training-data problems posed by many emerging applications (e.g., image retrieval, video surveillance, and gene profiling).

PBC4cip: A new contrast pattern-based classifier for class imbalance problems

Data Mining for Imbalanced Datasets: An Overview

  • N. Chawla
  • Computer Science
    The Data Mining and Knowledge Discovery Handbook
  • 2005
In this Chapter, some of the sampling techniques used for balancing the datasets, and the performance measures more appropriate for mining imbalanced datasets are discussed.

A Simple Approach to Ordinal Classification

This paper presents a simple method that enables standard classification algorithms to make use of ordering information in class attributes and shows that it outperforms the naive approach, which treats the class values as an unordered set.

Classification of Imbalanced Data: a Review

This paper provides a review of the classification of imbalanced data regarding the application domains, the nature of the problem, the learning difficulties with standard classifier learning algorithms; the learning objectives and evaluation measures; the reported research solutions; and the class imbalance problem in the presence of multiple classes.

Detecting Ordinal Subcascades

A novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations the authors have to examine is devised.

Sequence classification for credit-card fraud detection

Ordinal Classification: Working Definition and Detection of Ordinal Structures

This study proposes a working definition for OC tasks, which is based on the decision boundaries of standard binary Support Vector Machines, and introduces a simple algorithm for the detection of ordinal structures.

Support Vector Machines for Classification in Nonstandard Situations

This paper explains why the standard support vectors machine is not suitable for the nonstandard situation, and introduces a simple procedure for adapting the support vector machine methodology to the non standard situation.