In data mining and machine learning, the class imbalance problem occurs when the number of samples in one class (minority) is obviously smaller than the other one (majority), ignoring the learning of minority class. To solve this problem, many over-sampling methods, that fill the minority area with synthetic samples, have been proposed. But no one pure over-sampling method has been specially designed to fit complex distributions (such as noise, class overlapping and disjuncts). To fill this gap, this study firstly proposes one searchlight-scanned over-sampling method (SCOS), which tactfully treats the data filling of minority area as the searchlight scanning of objective area in real life. By respectively regarding the minority and majority area as the objective area and the barrier area of buildings, a series of searchlight structures are computed to firstly pass through corresponding minority area and then be stopped by the majority area. Finally, synthetic samples are generated in those structures. Implement on real-world datasets demonstrates the capability of our method to complex distributions, and the outperforming performance to current state-of-art over-sampling methods.

Searchlight-scanned Over-sampling for Class Imbalance Problem

Imbalanced problem, one significant challenge in data mining, occurs when the number of samples in one class (minority) is obviously smaller than the other one (majority). Over-sampling methods that generating new synthetic samples for the minority class have been proven to be effective. But rare over-sampling methods focus on the decision boundary between classes and none of them are proposed to directly compute the certain area of decision boundary for imbalanced problem. Thus, one novel method named Decision Boundary Computation-based Oversampling is proposed to fill this gap. The novel method employs the intuitive observation, that both boundary samples and their surrounding areas corporately constitute the decision boundary, to compute the partition belonging to the minority class by subtracting the partition of majority class from their corporate one. Which greatly enhancing the full use of boundary information brought by both boundary individuals and their near areas, and implicitly complement the nature information insufficiency of minority class at the same time. Finally, new synthetic samples are generated in the partition of decision boundary of minority class. Extensive experiments indicate the good performance of proposed method when compared with other state-of-art methods.

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Decision Boundary Computation-based Over-sampling for Imbalance Learning

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Searchlight-scanned Over-sampling for Class Imbalance Problem

Decision Boundary Computation-based Over-sampling for Imbalance Learning