A comparative study of FCA-based supervised classification algorithms

Huaiyu Fu, Huaiguo Fu, Patrik Njiwoua, Engelbert Mephu Nguifo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Citations (Scopus)

Abstract

Several FCA-based classification algorithms have been proposed, such as GRAND, LEGAL, GALOIS, RULEARNER, CIBLe, and CLNN & CLNB. These classifiers have been compared to standard classification algorithms such as C4.5, Naïve Bayes or IB1. They have never been compared each other in the same platform, except between LEGAL and CIBLe. Here we compare them together both theoretically and experimentally, and also with the standard machine learning algorithm C4.5. Experimental results are discussed.

Original languageEnglish
Title of host publicationConcept Lattices
EditorsPeter Eklund
PublisherSpringer
Pages313-320
Number of pages8
ISBN (Print)3540210431, 9783540210436
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event2nd International Conference on Formal Concept Analysis, ICFCA 2004 - Sydney, Australia
Duration: 23 Feb 200426 Feb 2004

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2961
ISSN (Print)0302-9743

Conference

Conference2nd International Conference on Formal Concept Analysis, ICFCA 2004
Country/TerritoryAustralia
CitySydney
Period23/02/200426/02/2004

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