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

• Autorzy: Koziarski M. , Woźniak M.

Identyfikatory

DOI

10.1515/amcs-2017-0050

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

machine learning; classification algorithms; imbalanced data; preprocessing; oversampling

PL

uczenie maszynowe; algorytm klasyfikacji; dane niezrównoważone; wstępne przetwarzanie danych

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2017
• Tom: Vol. 27, no. 4
• Strony: 727--736
• Opis fizyczny: Bibliogr. 51 poz., rys., tab., wykr.

Twórcy

autor

Koziarski M.

• Department of Systems and Computer Networks, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Woźniak M.

• Department of Systems and Computer Networks, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).