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This paper studies the imbalanced data classifycation problem and proposes bi-directional sampling based on clustering (BDSK) for the imbalanced data classification. This algorithm combines SMOTE over-sampling algorithm and under-sampling algorithm based on K-Means to solve the within-class imbalance problem and the between-class imbalance problem. It not only avoid induce too much noise but also...
Clustering is an important approach to the analysis of DNA microarray data. In this paper, we develop a new algorithm that can cluster DNA microarray data with a graph cut based algorithm. The algorithm can generate a list of clustering results with statistically significant likelihood. It can thus resolve the issue where a gene product may participate in different subsets of co-expressed genes. Our...
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