Creator:

Koziarski, Michał ; Woźniak, Michał

Contributor:

Stefanowski, Jerzy - ed. ; Krawiec, Krzysztof - ed. ; Wrembel, Robert - ed.

Title:

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

Subtitle:

.

Group publication title:

AMCS, Volume 27 (2017)

Subject and Keywords:

machine learning ; classification ; imbalanced data ; preprocessing ; oversampling

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2017

Resource Type:

artykuł

DOI:

10.1515/amcs-2017-0050

Pages:

727-736

Source:

AMCS, volume 27, number 4 (2017) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego