Not logged in : Login
(Sponging disallowed)

About: Automated Selection and Configuration of Multi-Label Classification Algorithms with Grammar-Based Genetic Programming     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : bibo:BookSection, within Data Space : linkeddata.uriburner.com:28898 associated with source document(s)

AttributesValues
type
seeAlso
sameAs
http://www.loc.gov...erms/relators/EDT
http://eprints.org/ontology/hasAccepted
http://eprints.org/ontology/hasDocument
dc:hasVersion
Title
  • Automated Selection and Configuration of Multi-Label Classification Algorithms with Grammar-Based Genetic Programming
described by
Date
  • 2018-08-21
Creator
status
Publisher
abstract
  • This paper proposes Auto-MEKAGGP, an Automated Machine Learning (Auto-ML) method for Multi-Label Classification (MLC) based on the MEKA tool, which offers a number of MLC algorithms. In MLC, each example can be associated with one or more class labels, making MLC problems harder than conventional (single-label) classification problems. Hence, it is essential to select an MLC algorithm and its configuration tailored (optimized) for the input dataset. Auto-MEKAGGP addresses this problem with two key ideas. First, a large number of choices of MLC algorithms and configurations from MEKA are represented into a grammar. Second, our proposed Grammar-based Genetic Programming (GGP) method uses that grammar to search for the best MLC algorithm and configuration for the input dataset. Auto-MEKAGGP was tested in 10 datasets and compared to two well-known MLC methods, namely Binary Relevance and Classifier Chain, and also compared to GA-AutoMLC, a genetic algorithm we recently proposed for the same task. Two versions of Auto-MEKAGGP were tested: a full version with the proposed grammar, and a simplified version where the grammar includes only the algorithmic components used by GA-Auto-MLC. Overall, the full version of Auto-MEKAGGP achieved the best predictive accuracy among all five evaluated methods, being the winner in six out of the 10 datasets.
Is Part Of
Subject
list of authors
list of editors
presented at
is topic of
is primary topic of
Faceted Search & Find service v1.17_git144 as of Jul 26 2024


Alternative Linked Data Documents: iSPARQL | ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 08.03.3331 as of Aug 25 2024, on Linux (x86_64-ubuntu_noble-linux-glibc2.38-64), Single-Server Edition (378 GB total memory, 37 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software