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Feature selection for supervised learning concerns the problem of selecting a number of important features (w.r.t. the class labels) for the purposes of training accurate prediction models. Traditional feature selection methods, however, fail to take the sample distributions into consideration which may lead to poor prediction for minority class examples. Due to the sophistication and the cost involved in the data collection process, many applications, such as biomedical research, commonly face biased data collections with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). For these applications, the minority class examples, such as disease samples, credit card frauds, and network intrusions, are only a small portion of the data but deserve full attention for accurate prediction. In this paper, we propose three filtering techniques, Higher Weight (HW), Differential Minority Repeat (DMR) and Balanced Minority Repeat (BMR), to identify important features from datasets with biased sample distribution. Experimental comparisons with the ReliefF method on five datasets demonstrate the effectiveness of the proposed methods in selecting informative features for accurate prediction of minority class examples. © 2010 World Scientific Publishing Company.

Feature selection concerns the problem of selecting a number of important features (w.r.t. the class labels) in order to build accurate prediction models. Traditional feature selection methods, however, fail to take the sample distributions into the consideration which may lead to poor predictions for minority class examples. Due to the sophistication and the cost involved in the data collection process, many applications, such as Biomedical research, commonly face biased data collections with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). For these applications, the minority class examples, such as disease samples, credit card frauds, and network intrusions, are only a small portion of the data collections but deserve full attentions for accurate prediction. In this paper, we propose three filtering techniques, Higher Weight (HW), Differential Minority Repeat (DMR) and Balanced Minority Repeat (BMR), to identify important features from biased data collections. Experimental comparisons with the ReliefF method on five datasets demonstrate the effectiveness of the proposed methods in selecting informative features from data with biased sample distributions.

Recent years have seen increasing quantities of high-throughput biological data available for genetic disease profiling, protein structure and function prediction, and new drug and therapy discovery. High-throughput biological experiments output high volume and/or high dimensional data, which impose significant challenges for molecular biologists and domain experts to properly and rapidly digest and interpret the data. In this paper, we provide simple background knowledge for computer scientists to understand how supervised learning tools can be used to solve biological challenges, with a primary focus on two types of problems: Biological sequence profiling and microarray expression data analysis. We employ a set of supervised learning methods to analyze four types of biological data: (1) gene promoter site prediction; (2) splice junction prediction; (3) protein structure prediction; and (4) gene expression data analysis. We argue that although existing studies favor one or two learning methods (such as Support Vector Machines), such conclusions might have been biased, mainly because of the inadequacy of the measures employed in their study. A line of learning algorithms should be considered in different scenarios, depending on the objective and the requirement of the applications, such as the system running time or the prediction accuracy on the minority class examples.