Embedding Feature Selection for Large-scale Hierarchical Classification
This work addresses efficiency challenges in large-scale hierarchical classification for domains like text and image processing, though it is incremental as it applies existing methods to new data.
The paper tackled the problem of large-scale hierarchical classification with high-dimensional features by investigating filter-based feature selection methods, achieving up to 3x speed-up and 45% less memory usage without significant accuracy loss.
Large-scale Hierarchical Classification (HC) involves datasets 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 HC 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 work, we investigate various filter-based feature selection methods for dimensionality reduction to solve the large-scale HC problem. Our experimental evaluation on text and image datasets with varying distribution of features, classes and instances shows upto 3x order of speed-up on massive datasets and upto 45% less memory requirements for storing the weight vectors of learned model without any significant loss (improvement for some datasets) in the classification accuracy. Source Code: https://cs.gmu.edu/~mlbio/featureselection.