DFC

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.

Towards an FCA-Based Approach for Explaining Multi-label Classification

“…Another frequent use is the generation of association rules, which also allows data analysis and knowledge extraction. In [16], an overview of classification methods based on formal concepts analysis is provided. In [2], the authors propose a learning classifier system (LCS) based on FCA to generate and exploit multi-label association rules which highlight the different relationships between labels.…”