Open AccessA Density-based Under-sampling Algorithm for Imbalance Classification
Author(s) -
Yuyang Hou,
Bailin Li,
Li Li,
Jiajia Li
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1302/2/022064
Subject(s) - class (philosophy) , computer science , sampling (signal processing) , noise (video) , noisy data , feature (linguistics) , artificial intelligence , data mining , algorithm , pattern recognition (psychology) , machine learning , space (punctuation) , linguistics , philosophy , filter (signal processing) , image (mathematics) , computer vision , operating system
Imbalance classification is an interesting issue in machine learning and data mining. In recent years, many related algorithms have been proposed to solve such an issue. Among them, under-sampling is an effective and timesaving data pre-processing method, which balances the dataset by removing some examples from the majority class. However, these proposed under-sampling methods often lose some useful information or ignore noise in the datasets, which will result in the performance degradation. This paper proposes a density-based under-sampling algorithm (DBU) to solve these two problems. In feature space, similar examples are close to each other and noisy example is far from other examples belonging to the same class. Thus the similar examples have a high density while the noisy example has a low density. DBU uses the local density peaks to represent the whole majority class, so that it can retain the useful information and eliminate the noisy examples automatically. To evaluate our algorithm, experiments are conducted on 15 two-class imbalanced datasets. Experimental results show that DBU achieves the better results than other under-sampling methods.