CNC-TP: Classifier Nominal Concept Based on Top-Pertinent Attributes

TL;DR

This paper reviews FCA-based classifiers in data mining, introduces a novel partial concept lattice construction method focusing on relevant concepts, and demonstrates its efficiency through experiments.

Contribution

It proposes a new approach for building partial concept lattices from nominal data, enhancing interpretability and efficiency of FCA-based classifiers.

Findings

The proposed method reduces computational complexity.

Experimental results show improved classification accuracy.

The approach effectively identifies relevant concepts.

Abstract

Knowledge Discovery in Databases (KDD) aims to exploit the vast amounts of data generated daily across various domains of computer applications. Its objective is to extract hidden and meaningful knowledge from datasets through a structured process comprising several key steps: data selection, preprocessing, transformation, data mining, and visualization. Among the core data mining techniques are classification and clustering. Classification involves predicting the class of new instances using a classifier trained on labeled data. Several approaches have been proposed in the literature, including Decision Tree Induction, Bayesian classifiers, Nearest Neighbor search, Neural Networks, Support Vector Machines, and Formal Concept Analysis (FCA). The last one is recognized as an effective approach for interpretable and explainable learning. It is grounded in the mathematical structure of the concept lattice, which enables the generation of formal concepts and the discovery of hidden relationships among them. In this paper, we present a state-of-theart review of FCA-based classifiers. We explore various methods for computing closure operators from nominal data and introduce a novel approach for constructing a partial concept lattice that focuses on the most relevant concepts. Experimental results are provided to demonstrate the efficiency of the proposed method.