Efficient feature size reduction via predictive forward selection

@article{Reif2014EfficientFS,
  title={Efficient feature size reduction via predictive forward selection},
  author={Matthias Reif and Faisal Shafait},
  journal={Pattern Recognition},
  year={2014},
  volume={47},
  pages={1664-1673}
}
Most of the widely used pattern classification algorithms, such as Support Vector Machines (SVM), are sensitive to the presence of irrelevant or redundant features in the training data. Automatic feature selection algorithms aim at selecting a subset of features present in a given dataset so that the achieved accuracy of the following classifier can be maximized. Feature selection algorithms are generally categorized into two broad categories: algorithms that do not take the following… CONTINUE READING
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