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Feature selection is an important preprocessing step when learning from bioinformatics datasets. Since these datasets often have high dimensionality (a large number of features), selecting the most important ones both improves performance and reduces computation time. In addition, when the features in question are genes (as is the case for microarray datasets), knowing the important genes is useful...
This paper presents a noise-based stability performance evaluation approach for feature selection techniques. For the stability assessment, a similarity-based measure is used to quantify the degree of agreement between a filter's output on a clean dataset and its outputs on the same dataset corrupted with different combinations of noise level and noise distribution. Experiments are conducted with...
Two of the most challenging problems in data mining are working with imbalanced datasets and with datasets which have a large number of attributes. In this study we compare three different approaches for handling both class imbalance and high dimensionality simultaneously. The first approach consists of sampling followed by feature selection, with the training data being built using the selected features...
Feature selection is an important component of data mining analysis with high dimensional data. Reducing the number of features in the dataset can have numerous positive implications, such as eliminating redundant or irrelevant features, decreasing development time and improving the performance of classification models. In this work, four filter-based feature selection techniques are compared using...
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