SciELO - Scientific Electronic Library Online

 
 issue54A Method Based on Genetic Algorithms for Generating Assessment Tests Used for LearningInstance Selection to Improve Gamma Classifier author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Polibits

On-line version ISSN 1870-9044

Abstract

SOHRAB, Mohammad Golam; MIWA, Makoto  and  SASAKI, Yutaka. IN-DEDUCTIVE and DAG-Tree Approaches for Large-Scale Extreme Multi-label Hierarchical Text Classification. Polibits [online]. 2016, n.54, pp.61-70. ISSN 1870-9044.  https://doi.org/10.17562/PB-54-8.

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, SVMper f, 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 onWikipedia 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.

Keywords : Hierarchical text classification; multi-label learning; indexing; extreme classification; tree-structured class hierarchy; DAG- or DG-structured class hierarchy.

        · text in English     · English ( pdf )