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Screening important variables for class-imbalanced data is still a challenging task. In this study, we propose an algorithm for stably selecting key variables on class-imbalanced data based on the precision-recall curve (PRC), where the PRC is utilized as the assessment criterion in the model building stage, and sparse regularized logistic regression combined with subsampling (SRLRS) is designed to perform stable variable selection. Considering the characteristic of class-imbalanced data, we also proposed classification-based partition for cross validation, as well as leaving half of majority observations out and leaving one minority observation out (LHO-LOO) for subsampling. Simulation results and real data showed that our algorithm is highly suitable for handling class-imbalanced data, and that the PRC can be an alternative evaluation criterion for model selection when handling class-imbalanced data. [ABSTRACT FROM AUTHOR]

Abstract: One of the main goals of metabolomics studies is to discover informative metabolites or biomarkers, which may be used to diagnose diseases and to find out pathology. Sophisticated feature selection approaches are required to extract the information hidden in such complex ‘omics’ data. In this study, it is proposed a new and robust selective method by combining random forests (RF) with model population analysis (MPA), for selecting informative metabolites from three metabolomic datasets. According to the contribution to the classification accuracy, the metabolites were classified into three kinds: informative, no-informative, and interfering metabolites. Based on the proposed method, some informative metabolites were selected for three datasets; further analyses of these metabolites between healthy and diseased groups were then performed, showing by T-test that the P values for all these selected metabolites were lower than 0.05. Moreover, the informative metabolites identified by the current method were demonstrated to be correlated with the clinical outcome under investigation. The source codes of MPA-RF in Matlab can be freely downloaded from http://code.google.com/p/my-research-list/downloads/list [Copyright &y& Elsevier]

Learning from class-imbalanced data has gained increasing attention in recent years due to the massive growth of skewed data across many scientific fields such as metabolomics. Some researches show that it is not the imbalance itself which hinders the classification performance, but class overlapping do play an important role in the performance degradation when associated to class-imbalance. So alleviating the overlapping of the imbalanced data might be an effective way to improve the performance in class-imbalance learning. In this study, we propose two feature selection algorithms that aim to minimize the overlap degree between the majority and the minority, which is based on a simple assumption that decreasing overlap degree of a data set makes it more separable. The proposed MOSNS and MOSS methods are built via sparse regularization techniques. Simulation results indicate that our algorithms is effective in recognizing key features and control false discoveries for class-imbalance learning. Four class-imbalanced metabolomics data sets are also employed to test the performance of our algorithm, and a comparison with accuracy (ACC)-based and ROC-based selection procedures is performed. The result shows that our algorithms are highly competitive and can be an alternative feature selection strategy in class-imbalance learning. ● Two feature selection algorithms are proposed by minimizing the overlap of a data set. ● Sparse feature selection strategy are used in class-imbalance learning. ● The classification performance of metabolomics data can be improved by sparse feature selection and re-balance preprocessing. [ABSTRACT FROM AUTHOR]