IN-DEDUCTIVE and DAG-Tree Approaches for Large-Scale Extreme Multi-label Hierarchical Text Classification

Abstract

This paper presents a large-scale extreme multilabel hierarchical text classification method that employs a large-scale hierarchical inductive learning and deductive classification (IN-DEDUCTIVE) approach using different efficient classifiers, and a DAG-Tree that refines the given hierarchy by eliminating nodes and edges to generate a new hierarchy. We evaluate our method on the standard hierarchical text classification datasets prepared for the PASCAL Challenge on Large-Scale Hierarchical Text Classification (LSHTC). We compare several classification algorithms on LSHTC including DCD-SVM, SVM perf , Pegasos, SGD-SVM, and Passive Aggressive, etc. Experimental results show that IN-DEDUCTIVE approach based systems with DCD-SVM, SGD-SVM, and Pegasos are promising and outperformed other learners as well as the top systems participated in the LSHTC3 challenge on Wikipedia medium dataset. Furthermore, DAG-Tree based hierarchy is effective especially for very large datasets since DAG-Tree exponentially reduce the amount of computation necessary for classification. Our system with IN-DEDUCIVE and DAG-Tree approaches outperformed the top systems participated in the LSHTC4 challenge on Wikipedia large dataset.

EDGE2VEC: Edge Representations for Large-Scale Scalable Hierarchical Learning

“…In general, ML-based TC can be categorized into two classification tasks: a flat classification (FC) [5,13,14,17] by referring to standard binary or multi-class classification problems where parent-child relations are completely omitted. Second is the hierarchical classification (HC) [15,16] -typically a tree, a directed acyclic graph (DAG), or a directed graph (DG) are incorporated, where the classes to be predicted are organized into a class hierarchy. A very large amount of research in TC, data mining (DM), and related researches have focused on FC problems.…”