Active learning for class imbalance problem

Ertekin, Şeyda; Huang, Jian; Giles, C. Lee

doi:10.1145/1277741.1277927

Cited by 140 publications

(80 citation statements)

References 3 publications

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“…Typically, the class imbalance ratio of examples that are close to the decision boundary is lower than the imbalance ratio in the complete data set. Therefore, the active learning approach provides more balanced training examples because it selects examples that lie closest to the separating hyperplane using the support vector machine algorithm [11], [12]. However, the method is designed based on the characteristics of the support vector machine algorithm; thus, the method cannot be applied to other classification algorithms.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification

Lee

Zhang

2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-In biomedical data, the imbalanced data problem occurs frequently and causes poor prediction performance for minority classes. It is because the trained classifiers are mostly derived from the majority class. In this paper, we describe an ensemble learning method combined with active example selection to resolve the imbalanced data problem. Our method consists of three key components: 1) an active example selection algorithm to choose informative examples for training the classifier, 2) an ensemble learning method to combine variations of classifiers derived by active example selection, and 3) an incremental learning scheme to speed up the iterative training procedure for active example selection. We evaluate the method on six real-world imbalanced data sets in biomedical domains, showing that the proposed method outperforms both the random under sampling and the ensemble with under sampling methods. Compared to other approaches to solving the imbalanced data problem, our method excels by 0.03-0.15 points in AUC measure.

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Section: Related Workmentioning

confidence: 99%

Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification

Lee

Zhang

2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…Recently, various approaches on active learning from imbalanced data sets have been proposed in literature [1] [22] [23] [24]. In particular, an active learning method based on support vector machines (SVM) was proposed in [23] [24]. Instead of searching the entire training data space, this method can effectively select informative instances from a random set of training populations, therefore significantly reducing the computational cost when dealing with large imbalanced data sets.…”

Section: Introductionmentioning

confidence: 99%

ADASYN: Adaptive synthetic sampling approach for imbalanced learning

Bai

Garcia

et al. 2008

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

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462

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Abstract-This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minority class examples according to their level of difficulty in learning, where more synthetic data is generated for minority class examples that are harder to learn compared to those minority examples that are easier to learn. As a result, the ADASYN approach improves learning with respect to the data distributions in two ways: (1) reducing the bias introduced by the class imbalance, and (2) adaptively shifting the classification decision boundary toward the difficult examples. Simulation analyses on several machine learning data sets show the effectiveness of this method across five evaluation metrics.

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“…Seyda Ertekin et.al. developed an efficient method [30] for active selection of informative instances from a randomly picked small pool of samples. In the proposed method they have maintained the same or achieving even higher g-means values by using less number of training instances in the SVM model.…”

Section: Introductionmentioning

confidence: 99%

On the Classification of Imbalanced Datasets

N¹,

Sheshadri²

2012

IJCA

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In recent research the classifications of imbalanced data sets have received considerable attention. It is natural that due to the class imbalance the classifier tends to favour majority class. In this paper we investigate the performance of different methods for handling data imbalance in the microcalcification classification which is a classical example for data imbalance problem. Micro calcifications are very tiny deposits of calcium that appear as small bright spots in the mammogram. Classification of microcalcification clusters from mammograms plays an important role in computer-aided diagnosis for early detection of breast cancer. In this paper, we review in brief the state of the art techniques in the framework of imbalanced data sets and investigate the performance of different methods for microcalcification classification.

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Active learning for class imbalance problem

Abstract: The class imbalance problem has been known to hinder the learning performance of classification algorithms. Various real-world classification tasks such as text categorization suffer from this phenomenon. We demonstrate that active learning is capable of solving the problem.

Cited by 140 publications

References 3 publications

Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification

Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification

ADASYN: Adaptive synthetic sampling approach for imbalanced learning

On the Classification of Imbalanced Datasets

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