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Found 4 papers, 0 papers with code
Embedding Feature Selection for Large-scale Hierarchical Classification
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.
Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning
Multi-task learning (MTL) is a supervised learning paradigm in which the prediction models for several related tasks are learned jointly to achieve better generalization performance.
Inconsistent Node Flattening for Improving Top-down Hierarchical Classification
In this paper, we propose two different data-driven approaches (local and global) for hierarchical structure modification that identifies and flattens inconsistent nodes present within the hierarchy.
Filter based Taxonomy Modification for Improving Hierarchical Classification
Experimental comparisons of top-down HC with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i:e:, defined by expert), clustered hierarchy and flattening based hierarchy modification approaches.
