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Analysis conducted for bioinformatics applications often requires the use of feature selection methodologies to handle datasets with very high dimensionality. We propose 11 new threshold-based feature selection techniques and compare the performance of these new techniques to that of six standard filter-based feature selection procedures. Unlike other comparisons of feature selection techniques, we...
Various feature selection techniques have been proposed in the field of machine learning. The filter approaches are typically faster while wrapper approaches are more reliable though computationally expensive. Feature selection techniques often strive to achieve performance similar to wrapper approaches employing various computational approaches. Feature selection techniques typically depend on ways...
Machine learning can be broadly divided into supervised and unsupervised learning (Hastie et al. in The elements of statistical learning, Springer, New York, 2009). In supervised learning which is also known as classification, a classifier learns from some objects with known class labels and later assigns class labels to unknown objects based on acquired knowledge (Kotsiantis et al. in Proceedings...
One of the most important problems in the context of systems biology is to infer gene regulatory networks from gene expression data, since most of the control of cellular processes are performed by the multivariate activity of genes by means of their transcribed mRNA expression. Although many methods have been proposed to deal with this problem in the last two decades, gene network inference from...
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