Hybrid frameworks that integrate rule-based systems with machine learning (ML) have gained significant attention for their ability to combine the strengths of both paradigms, addressing the limitations of individual approaches. Rule-based systems, known for their interpretability and domain expertise incorporation, provide structured decision-making, while machine learning algorithms offer robust data-driven insights and adaptability. This chapter explores the fusion of these two methodologies to automate anomaly analysis, enhance system efficiency, and improve decision accuracy across various complex domains. The focus lies on evolutionary algorithms for optimizing rule-based components, reinforcement learning to refine decision-making policies, and bridging the interpretability gap between data scientists and domain experts. Additionally, the chapter discusses the critical role of explainability, emphasizing transparency mechanisms that foster trust and collaboration between technical and non-technical stakeholders. By integrating machine learning with rule-based systems, the proposed frameworks contribute to real-time, scalable solutions with enhanced adaptability and interpretability. This hybrid approach has profound implications in fields such as healthcare, autonomous systems, and cybersecurity, where both accuracy and transparency are paramount.ÂÂÂ
Hybrid frameworks that merge rule-based systems with machine learning (ML) are emerging as advanced solutions capable of addressing the inherent limitations of each individual approach [1]. Rule-based systems, traditionally relied upon for their clarity and interpretability, leverage expert knowledge to make decisions based on a predefined set of rules [2]. These systems excel in domains requiring human-like reasoning and transparent decision processes. Their limitations become evident when faced with dynamic and large-scale data sets, where complex patterns and relationships may emerge beyond the scope of the original rule set [3]. Machine learning algorithms, particularly those leveraging deep learning and reinforcement learning, provide the flexibility required to adapt to new data and identify intricate patterns [4]. While these models excel in prediction and adaptation, they often suffer from opacity, making it difficult for users to understand how decisions are made. Integrating rule-based systems with machine learning offers a solution that combines the interpretability of expert-driven rules with the adaptability of data-driven models, paving the way for a more robust and scalable decision-making process [5].
The combination of rule-based systems and machine learning has particular significance in fields requiring automated anomaly detection, where real-time insights and accurate decision-making are crucial [6]. Anomaly detection tasks, which involve identifying deviations from normal patterns in large datasets, are common in industries such as healthcare, cybersecurity, and finance [7]. In these domains, hybrid systems can automate the identification of unusual patterns, enabling faster responses to potential threats, whether they are medical anomalies, fraud detection, or system security breaches [8]. Rule-based systems can provide the initial framework for identifying known anomalies based on expert-defined patterns, while machine learning can complement this by dynamically adapting the model to recognize new, unseen anomalies through continuous learning from data [9]. This synergy enhances system reliability by continuously evolving the decision-making rules without requiring manual intervention, making hybrid systems highly adaptable to changing data conditions and complex environments [10].
