Adaptive Ensemble Undersampling-Boost: A novel learning framework for imbalanced data

Lü, Wei; Li, Zhe; Chu, Jinghui

doi:10.1016/j.jss.2017.07.006

Cited by 59 publications

(28 citation statements)

References 29 publications

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“…Another family of methods that can be distinguished are the cluster-based undersampling algorithms, notably the methods proposed by Yen and Lee [30], which use clustering to select the most representative subset of data. Finally, as has been originally demonstrated by Liu et al [31], undersampling algorithms are well-suited for forming classifier ensembles, an idea that was further extended in form of evolutionary undersampling [32] and boosting [33].…”

Section: Guided Undersampling Strategiesmentioning

confidence: 98%

Radial-Based Undersampling for imbalanced data classification

Koziarski

2020

Pattern Recognition

133

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Data imbalance remains one of the most widespread problems affecting contemporary machine learning. The negative effect data imbalance can have on the traditional learning algorithms is most severe in combination with other dataset difficulty factors, such as small disjuncts, presence of outliers and insufficient number of training observations. Said difficulty factors can also limit the applicability of some of the methods of dealing with data imbalance, in particular the neighborhood-based oversampling algorithms based on SMOTE. Radial-Based Oversampling (RBO) was previously proposed to mitigate some of the limitations of the neighborhood-based methods. In this paper we examine the possibility of utilizing the concept of mutual class potential, used to guide the oversampling process in RBO, in the undersampling procedure. Conducted computational complexity analysis indicates a significantly reduced time complexity of the proposed Radial-Based Undersampling algorithm, and the results of the performed experimental study indicate its usefulness, especially on difficult datasets.

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Section: Guided Undersampling Strategiesmentioning

confidence: 98%

Radial-Based Undersampling for imbalanced data classification

Koziarski

2020

Pattern Recognition

133

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“…Ensemble learning is one of the most popular methods at present. It has near-optimal classification methods for any problem, and it can achieve better generalization performance than a single classifier by training multiple individual classifiers and combining them together [8,[20][21][22][23][24][25][26][27][28][29][30][31][32]. There are two main approaches of ensemble learning: Bagging and Boosting.…”

Section: Related Workmentioning

confidence: 99%

A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data

et al. 2020

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Imbalanced classification is one of the most important problems of machine learning and data mining, existing in many real datasets. In the past, many basic classifiers such as SVM, KNN, and so on have been used for imbalanced datasets in which the number of one sample is larger than that of another, but the classification effect is not ideal. Some data preprocessing methods have been proposed to reduce the imbalance ratio of data sets and combine with the basic classifiers to get better performance. In order to improve the whole classification accuracy, we propose a novel classifier ensemble framework based on K-means and resampling technique (EKR). First, we divide the data samples in the majority class into several sub-clusters using K-means, k-value is determined by Average Silhouette Coefficient, and then adjust the number of data samples of each sub-cluster to be the same as that of the minority classes through resampling technology, after that each adjusted sub-cluster and the minority class are combined into several balanced subsets, the base classifier is trained on each balanced subset separately, and finally integrated into a strong ensemble classifier. In this paper, the extensive experimental results on 16 imbalanced datasets demonstrate the effectiveness and feasibility of the proposed algorithm in terms of multiple evaluation criteria, and EKR can achieve better performance when compared with several classical imbalanced classification algorithms using different data preprocessing methods.

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“…We summarize the relevant works from these two aspects. From the perspective of majority samples, the extraction of representative samples is the main work, called under-sampling [Lu, Li and Chu (2017)]. Two common specific methods are Ensemble method [Ren, Cao, Li et al (2017)] and Cascade method [Kotsiantis (2011)].…”

Section: Related Workmentioning

confidence: 99%

Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection

Luo¹,

Wang²,

Cai³

et al. 2019

Computers, Materials &Amp; Continua

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The extreme imbalanced data problem is the core issue in anomaly detection. The amount of abnormal data is so small that we cannot get adequate information to analyze it. The mainstream methods focus on taking fully advantages of the normal data, of which the discrimination method is that the data not belonging to normal data distribution is the anomaly. From the view of data science, we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method. In this kind of technologies, Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones, which generate synthetic examples randomly in selected line segments. In our work, we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data method. In theory, our method covers a wider range. In experiment with real world data, our method performs better than the SMOTE and its meliorations.

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Adaptive Ensemble Undersampling-Boost: A novel learning framework for imbalanced data

Cited by 59 publications

References 29 publications

Radial-Based Undersampling for imbalanced data classification

Radial-Based Undersampling for imbalanced data classification

A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data

Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection

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