Knowledge-Based Machine Learning Models for Decision Support Systems

Abstract

Decision Support Systems (DSS) are widely used in healthcare, finance, manufacturing, education, transportation, and public administration to support data-driven decision-making. Traditional DSS based on rule-based expert systems and statistical models often struggle to adapt to dynamic and complex environments. To address these limitations, Knowledge-Based Machine Learning (KBML) integrates machine learning with symbolic knowledge representation techniques such as ontologies, semantic networks, expert rules, and domain constraints. By incorporating prior knowledge into the learning process, KBML enhances reasoning, interpretability, transparency, and predictive performance while reducing training requirements. This paper reviews knowledge-based machine learning approaches for intelligent DSS and examines the integration of knowledge engineering principles with supervised, unsupervised, reinforcement, and ensemble learning methods. The roles of ontologies, rule-based inference, semantic reasoning, and knowledge graphs in improving learning effectiveness are also discussed. A comprehensive DSS framework is proposed, consisting of knowledge acquisition, data preprocessing, feature engineering, knowledge representation, model training, inference generation, and decision recommendation modules. Experimental results demonstrate that knowledge-enhanced models achieve higher accuracy, improved decision consistency, reduced uncertainty, and greater interpretability than conventional machine learning approaches. The study also highlights challenges related to knowledge acquisition, scalability, ontology maintenance, and system integration. Future research directions include explainable AI, deep knowledge graphs, federated learning, cognitive computing, and autonomous reasoning systems.