<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In this paper we describe results of an experimental study where we checked the impact of various difficulty factors in imbalanced data sets on the performance of selected classifiers applied alone or combined with several preprocessing methods. In the study we used artificial data sets in order to systematically check factors such as dimensionality, class imbalance ratio or distribution of specific types of examples (safe, borderline, rare and outliers) in the minority class. The results revealed that the latter factor was the most critical one and it exacerbated other factors (in particular class imbalance). The best classification performance was demonstrated by non-symbolic classifiers, particular by k-NN classifiers (with 1 or 3 neighbors - 1NN and 3NN, respectively) and by SVM. Moreover, they benefited from different preprocessing methods - SVM and 1NN worked best with undersampling, while oversampling was more beneficial for 3NN.</p></abstract>

In this paper we describe results of an experimental study where we checked the impact of various difficulty factors in imbalanced data sets on the performance of selected classifiers applied alone or combined with several preprocessing methods. In the study we used artificial data sets in order to systematically check factors such as dimensionality, class imbalance ratio or distribution of specific types of examples (safe, borderline, rare and outliers) in the minority class. The results revealed that the latter factor was the most critical one and it exacerbated other factors (in particular class imbalance). The best classification performance was demonstrated by non-symbolic classifiers, particular by k-NN classifiers (with 1 or 3 neighbors - 1NN and 3NN, respectively) and by SVM. Moreover, they benefited from different preprocessing methods - SVM and 1NN worked best with undersampling, while oversampling was more beneficial for 3NN.

Difficulty Factors and Preprocessing in Imbalanced Data Sets: An Experimental Study on Artificial Data

Foundations of Computing and Decision Sciences

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

In this paper we describe results of an experimental study where we checked the impact of various difficulty factors in imbalanced data sets on the performance...

Difficulty Factors and Preprocessing in Imbalanced Data Sets: An Experimental Study on Artificial Data

Data publikacji: 16 cze 2017

Zakres stron: 149 - 176

Otrzymano: 19 paź 2016

Przyjęty: 24 kwi 2017

DOI: https://doi.org/10.1515/fcds-2017-0007

Słowa kluczowe
imbalanced data, difficulty factors, preprocessing methods, learning and classification

© by Szymon Wilk

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

Difficulty Factors and Preprocessing in Imbalanced Data Sets: An Experimental Study on Artificial Data

Data publikacji: 16 cze 2017

Zakres stron: 149 - 176

Otrzymano: 19 paź 2016

Przyjęty: 24 kwi 2017

DOI: https://doi.org/10.1515/fcds-2017-0007

Słowa kluczoweimbalanced data, difficulty factors, preprocessing methods, learning and classification

© by Szymon Wilk

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

Słowa kluczowe
imbalanced data, difficulty factors, preprocessing methods, learning and classification