Autor:

Koziarski, Michał ; Woźniak, Michał

Współtwórca:

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

Tytuł:

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

Podtytuł:

.

Tytuł publikacji grupowej:

AMCS, Volume 27 (2017)

Temat i słowa kluczowe:

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2017

Typ zasobu:

artykuł

DOI:

10.1515/amcs-2017-0050

Strony:

727-736

Źródło:

AMCS, volume 27, number 4 (2017) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego