BigDataFr recommends: Hierarchical Label Partitioning for Large Scale Classification

Abstract

Extreme classification task where the number of classes is very large has received important focus over the last decade. Usual efficient multi-class classification approaches have not been designed to deal with such large number of classes. A particular issue in the context of large scale problems concerns the computational classification complexity : best multi-class approaches have generally a linear complexity with respect to the number of classes which does not allow these approaches to scale up. Recent works have put their focus on using hierarchical classification process in order to speed-up the classification of new instances. A priori information on labels is not always available nor useful to build hierarchical models. Finding a suitable hierarchical organization of the labels is thus a crucial issue as the accuracy of the model depends highly on the label assignment through the label tree. We propose in this work a new algorithm to build iteratively a hierarchical label structure by proposing a partitioning algorithm which optimizes simultaneously the structure in terms of classification complexity and the label partitioning problem in order to achieve high classification performances. [..]

Read paper
By Raphael Puget ¹ Nicolas Baskiotis ¹
Source: hal.archives-ouvertes.fr
1 MLIA – Machine Learning and Information Access
LIP6 – Laboratoire d’Informatique de Paris 6