Abstract
LSHC involves dataset consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-scale problem. It speeds up the training process, reduces the prediction time, and minimizes the memory requirements by compressing the total size of learned model weight vectors. Majority of the studies have also shown feature selection to be competent and successful in improving the classification accuracy by removing irrelevant features. In this chapter, we investigate various filter-based feature selection methods for dimensionality reduction to solve the LSHC problem.
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Naik, A., Rangwala, H. (2018). Large-Scale Hierarchical Classification with Feature Selection. In: Large Scale Hierarchical Classification: State of the Art. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-01620-3_4
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DOI: https://doi.org/10.1007/978-3-030-01620-3_4
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