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.
Highlights
In this paper, we are interested in classification
Formal Concept Analysis is an interesting formalism to study machine learning and classification methods. It allows a full construction of the concepts and the dependence relationships between concepts to build a lattice of Formal Concepts
We report common limits among all the supervised learning methods based on Formal Concept Analysis: absence of the adaptive aspect and the generation of concepts is either exhaustive or non-contextual
Summary
We are interested in classification. Classification is a two-phase process: a learning phase which organizes the information extracted from a set of objects (or data) and a classification phase which determines the label/class of new objects. We propose to use this technique in this work to study the classifier ensembles based on formal concepts, since a limited number of studies have focused on the formal concepts in the context of parallel learning. A critical overview of algorithms based on the extraction of formal concepts shows that existing algorithms in literature failed in their objectives Almost all these algorithms have been focused on the extraction of all concepts of concept lattice, which increases the costs of extraction of classification rules and makes their use almost impossible for large databases. The major difference between lattice and sub-lattice based classification is the number of concepts generated Their limit is the possible loss of information in a condensed data representation or a partial reproduction of the complete lattice. We notice that with those methods based on sublattice classification, the constructed concepts are chosen based on inappropriate criteria (i.e., the depth of the lattice, the covering of the context, etc.) (Meddouri & Maddouri, 2009)
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