<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Imbalanced data classification is one of the most widespread challenges in contemporary pattern recognition. Varying levels of imbalance may be observed in most real datasets, affecting the performance of classification algorithms. Particularly, high levels of imbalance make serious difficulties, often requiring the use of specially designed methods. In such cases the most important issue is often to properly detect minority examples, but at the same time the performance on the majority class cannot be neglected. In this paper we describe a novel resampling technique focused on proper detection of minority examples in a two-class imbalanced data task. The proposed method combines cleaning the decision border around minority objects with guided synthetic oversampling. Results of the conducted experimental study indicate that the proposed algorithm usually outperforms the conventional oversampling approaches, especially when the detection of minority examples is considered.</p></abstract>

Imbalanced data classification is one of the most widespread challenges in contemporary pattern recognition. Varying levels of imbalance may be observed in most real datasets, affecting the performance of classification algorithms. Particularly, high levels of imbalance make serious difficulties, often requiring the use of specially designed methods. In such cases the most important issue is often to properly detect minority examples, but at the same time the performance on the majority class cannot be neglected. In this paper we describe a novel resampling technique focused on proper detection of minority examples in a two-class imbalanced data task. The proposed method combines cleaning the decision border around minority objects with guided synthetic oversampling. Results of the conducted experimental study indicate that the proposed algorithm usually outperforms the conventional oversampling approaches, especially when the detection of minority examples is considered.

CCR: A combined cleaning and resampling algorithm for imbalanced data classification

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Imbalanced data classification is one of the most widespread challenges in contemporary pattern recognition. Varying levels of imbalance may be observed in...

CCR: A combined cleaning and resampling algorithm for imbalanced data classification

Publicado en línea: 13 ene 2018

Páginas: 727 - 736

Recibido: 12 ene 2016

Aceptado: 25 ago 2017

DOI: https://doi.org/10.1515/amcs-2017-0050

Palabras clave
machine learning, classification, imbalanced data, preprocessing, oversampling

© by Michał Koziarski

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

CCR: A combined cleaning and resampling algorithm for imbalanced data classification

Publicado en línea: 13 ene 2018

Páginas: 727 - 736

Recibido: 12 ene 2016

Aceptado: 25 ago 2017

DOI: https://doi.org/10.1515/amcs-2017-0050

Palabras clavemachine learning, classification, imbalanced data, preprocessing, oversampling

© by Michał Koziarski

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
machine learning, classification, imbalanced data, preprocessing, oversampling