FI-CSP-OM: A Hybrid Fuzzy Inference and Constraint Satisfaction Approach for Oversampling
TL;DR
A new hybrid framework that combines Fuzzy Inference Systems, Constraint Satisfaction Problems, and Optimization Modeling together for analysing and interpreting imbalanced datasets offers a powerful and interpretable approach for handling imbalanced classification problems by combining fuzzy logic, data mining, and constraint-based modeling.
Abstract
In this paper, we describe a new hybrid framework called FIS-CSP-OM that combines Fuzzy Inference Systems (FIS), Constraint Satisfaction Problems (CSP), and Optimization Modeling (OM) together for analysing and interpreting imbalanced datasets. The goal of the hybrid framework is to provide a means to integrate interpretable rule-based models (e.g., FIS) and numerical constraint-based reasoning (e.g., CSP and OM) in order to better represent imbalanced datasets and to understand the relationships among the variables used to create the model. Using triangular membership functions, we convert all of the numerical features into their corresponding linguistic variables, so that we can represent the dataset in a fuzzy manner. After the linguistic representation of the data is generated, we use the Apriori algorithm to perform association rule mining which enables us to create a set of high-confidence rules that can be used to describe and characterise the minority class. Finally, we can use the set of rules to develop a Linguistic CSP model that consists of symbolic variables that represent all of the linguistic features of the data and that have constraints that describe the relationships that were uncovered using the high-confidence rules. Following the transformation of the linguistic model into a Numerical CSP by identifying fuzzy modalities with continuous intervals, a tractable question will be derived from Constraint Solvers. Validation of the methodology is performed on the imbalanced dataset where the focus is on the minority class, ‘young.’ Experimental results demonstrate that there is a high degree of confidence in the extracted rules and a great deal of structural coherence between them, yielding an accurate, interpretable characterization of the target class. In addition, the CSP allows for a geometric interpretation of feasible regions in the feature space and the production of synthetic instances consistent with the constraints learned. Overall, the FIS-CSP-OM framework offers a powerful and interpretable approach for handling imbalanced classification problems by combining fuzzy logic, data mining, and constraint-based modeling. It opens new perspectives for explainable artificial intelligence, knowledge extraction, and constraint-driven data generation.