🤖 AI Summary
This study addresses the challenge of balancing efficiency and interpretability in nominal data classification by proposing a novel approach grounded in Formal Concept Analysis (FCA). The method leverages closure operators to identify and retain only those concepts corresponding to Top-Pertinent attributes—i.e., attributes most relevant to the classification task—thereby constructing a lightweight partial concept lattice. This strategy substantially reduces the structural complexity of the lattice while preserving semantic interpretability. Experimental results across multiple datasets demonstrate that the proposed approach outperforms existing FCA-based baselines, achieving a superior trade-off between classification performance and model interpretability.
📝 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-the-art 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.