Discover Class-based Feature Distribution by Encoding Discrete Data for Classification

The self-organization map is an unsupervised learning technique that discovers patterns and relationships in data without requiring labeled training data. Inspired by the self-organization map, Self-Organization Granular encoding has been proven effective for generating reliable discrete data clustering results as it is a data encoding technique that uses fuzzy sets and granularity to handle uncertain and imprecise information within discrete data. However, it is mainly useful for unsupervised learning, and its feasibility for supervised learning has not been studied yet. Also, discrete data classification is still under-researched. This paper proposes a new discrete data classification method called Transposed Fuzzy Class Granular classification. This method aims to transform discrete data into fuzzy partitions by considering all available classes and generating representations of the trained class’s Transposed Fuzzy Class Granular distribution by measuring the total divergence to the average of each fuzzy class’s membership degree distribution. The paper introduces a novel approach to discrete data classification by adapting Class Granules for classification and improving performance by tackling uncertainty, ambiguity, and the unique characteristics of discrete datasets. The study examined seven discrete datasets and compared their performance with eight commonly used classifiers as the baseline. These datasets were naturally discrete or created by discrete partitions of real datasets. The experimental results demonstrate that the proposed classifier outperforms the baseline classifiers in discrete data classification.