FCA-BASED RULE GENERATOR, a framework for the genetic generation of fuzzy classification systems using formal concept analysis

Cintra, Marcos E.; Monard, Maria Carolina; Camargo, Heloisa de Arruda

doi:10.1109/fuzz-ieee.2015.7337950

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…The semantic richness is guaranteed, and so, FCA facilitates the task of the classifier in knowledge discovery. Many learning methods based on Formal Concept Analysis are proposed, such as GRAND (Oosthuizen, 1988), LEGAL (Liquiere & Nguifo, 1990), GALOIS (Carpineto & Romano, 1993), RULEARNER (Sahami, 1995), CIBLe (Njiwoua & Nguifo, 1999), CLNN&CLNB (Xie & al., 2002), IPR (Maddouri, 2004), NAVIGALA (Visani & al., 2011), CITREC (Douar & al., 2008), CBALattice (Gupta & al., 2005), HMCS-FCA-SC (Ferrandin & al., 2013), SPFC (Ikeda & Yamamotol., 2013), CLANN (Nguifo et al., 2008), FCA-BRG (Cintra et al, 2015) and RMCS (Kashnitsky & Ignatov, 2015).…”

Section: Formal Concept Analysis and Classificationmentioning

confidence: 99%

DFC

Meddouri

Khoufi²,

Maddouri

2021

International Journal of Artificial Intelligence and Machine Learning

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Knowledge discovery data (KDD) is a research theme evolving to exploit a large data set collected every day from various fields of computing applications. The underlying idea is to extract hidden knowledge from a data set. It includes several tasks that form a process, such as data mining. Classification and clustering are data mining techniques. Several approaches were proposed in classification such as induction of decision trees, Bayes net, support vector machine, and formal concept analysis (FCA). The choice of FCA could be explained by its ability to extract hidden knowledge. Recently, researchers have been interested in the ensemble methods (sequential/parallel) to combine a set of classifiers. The combination of classifiers is made by a vote technique. There has been little focus on FCA in the context of ensemble learning. This paper presents a new approach to building a single part of the lattice with best possible concepts. This approach is based on parallel ensemble learning. It improves the state-of-the-art methods based on FCA since it handles more voluminous data.

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Section: Formal Concept Analysis and Classificationmentioning

confidence: 99%

DFC

Meddouri

Khoufi²,

Maddouri

2021

International Journal of Artificial Intelligence and Machine Learning

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show abstract

“…Rule-based classification approaches aim to achieve classification tasks by certain types of acquired rules, such as fuzzy rules [1][2][3][4] and formal concept analysis (FCA)-based [5] rules [6]. One of the biggest challenges faced by such classification methods is how to mine useful information from massive data to improve the classification ability of rules in terms of the speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…, u} is a granular consistent set. (6) The granular reduct set G + 6 is preserved after the objects x 9 and x 10 are added into the updated formal decision context.…”

mentioning

confidence: 99%