DFC: A Performant Dagging Approach of Classification Based on Formal Concept

Nida Meddouri, Hela Khoufi, Mondher Maddouri

Source Title: International Journal of Artificial Intelligence and Machine Learning (IJAIML)11(2)

ISSN: 2642-1577|EISSN: 2642-1585|EISBN13: 9781799864110|DOI: 10.4018/IJAIML.20210701.oa3

MLA

Meddouri, Nida, et al. "DFC: A Performant Dagging Approach of Classification Based on Formal Concept." IJAIML vol.11, no.2 2021: pp.38-62. http://doi.org/10.4018/IJAIML.20210701.oa3

APA

Meddouri, N., Khoufi, H., & Maddouri, M. (2021). DFC: A Performant Dagging Approach of Classification Based on Formal Concept. International Journal of Artificial Intelligence and Machine Learning (IJAIML), 11(2), 38-62. http://doi.org/10.4018/IJAIML.20210701.oa3

Chicago

Meddouri, Nida, Hela Khoufi, and Mondher Maddouri. "DFC: A Performant Dagging Approach of Classification Based on Formal Concept," International Journal of Artificial Intelligence and Machine Learning (IJAIML) 11, no.2: 38-62. http://doi.org/10.4018/IJAIML.20210701.oa3

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

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.