Adaptive weighted oversampling for imbalanced datasets based on density peaks clustering with heuristic filtering Abstract   Learning from imbalanced datasets poses a major challenge in data mining community. When dealing with imbalanced datasets, conventional classification algorithms generally perform poorly as they are originally designed to work under balanced class distribution scenarios. Although there exist different methods to addressing this issue, sampling methods especially oversampling techniques have shown great potentials as they aim to improve datasets itself rather than the classifiers, which can allow them to be used for any classifier. In this paper, we propose a novel adaptive weighted oversampling for imbalanced datasets based on density peaks clustering with heuristic filtering. Unlike other clusteringbased oversampling methods, the proposed approach applies modified density peaks clustering rather than traditional kmeans clustering techniques to cluster the minority instances due to its capability of accurately identifying subclusters with different sizes and densities, which is beneficial for the proposed method to simultaneously accommodate for betweenclass and withinclass imbalance issues caused by various reasons. Subsequently, the size for each identified subcluster to be oversampled is adaptively determined according to its own size and density and then the minority instances within each subcluster are oversampled based on their probabilities inversely proportional to their distances to the majority class and their densities with the aim of generating more synthetic minority instances for borderline and sparser ones. Finally, in order to avoid the generation of overlapping, a heuristic filtering strategy is also developed to iteratively move the possibly overlapped minority instances away from the majority class. The extensive experimental results on the different imbalanced datasets demonstrate that the proposed approach can achieve better classification performance in most datasets as compared to the other existing oversampling techniques.

An UnderSampling Method with Support Vectors in Multiclass Imbalanced Data Classification Multiclass imbalanced data classification in supervised learning is one of the most challenging research issues in machine learning for data mining applications. Although several data sampling methods have been introduced by computational intelligence researchers in the past decades for handling imbalanced data, still learning from imbalanced data is a challenging task and played as a significant focused research interest as well. Traditional machine learning algorithms usually biased to the majority class instances whereas ignored the minority class instances. As a result, ignoring minority class instances may affect the prediction accuracy of classifiers. Generally, undersampling and oversampling methods are commonly used in single model classifiers or ensemble learning for dealing with imbalanced data. In this paper, we have introduced an undersampling method with support vectors for classifying imbalanced data. The proposed approach selects the most informative majority class instances based on the support vectors that help to engender decision boundary. We have tested the performance of the proposed method with single classifiers (C4.5 Decision Tree classifier and naive Bayes classifier) and ensemble classifiers (Random Forest and AdaBoost) on 13 benchmark imbalanced datasets. It is explicitly shown by the experimental result that the proposed method produces high accuracy when classifying both the minority and majority class instances compared to other existing methods.