Multi-label classification and extracting predicted class hierarchies

被引：26

作者：

Brucker, Florian ^{[1
]}

Benites, Fernando ^{[1
]}

Sapozhnikova, Elena ^{[1
]}

机构：

[1] Univ Konstanz, Dept Comp & Informat Sci, D-78457 Constance, Germany

来源：

PATTERN RECOGNITION | 2011年 / 44卷 / 03期

关键词：

Multi-label classification; Hierarchy extraction; Text mining; Adaptive resonance theory (ART); NEURAL-NETWORK ARCHITECTURE; ARTMAP; RECOGNITION; DISCOVERY;

D O I：

10.1016/j.patcog.2010.09.010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper investigates hierarchy extraction from results of multi-label classification (MC). MC deals with instances labeled by multiple classes rather than just one, and the classes are often hierarchically organized. Usually multi-label classifiers rely on a predefined class hierarchy. A much less investigated approach is to suppose that the hierarchy is unknown and to infer it automatically. In this setting, the proposed system classifies multi-label data and extracts a class hierarchy from multi-label predictions. It is based on a combination of a novel multi-label extension of the fuzzy Adaptive Resonance Associative Map (ARAM) neural network with an association rule learner. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：724 / 738

页数：15

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