Machine Learning-Driven Classification for Enhanced Rule Proposal Framework

作     者：B.Gomathi R.Manimegalai Srivatsan Santhanam Atreya Biswas 

作者机构：Department of Computer Science and EngineeringPSG Institute of Technology and Applied ResearchCoimbatore641062India SAP Labs India Private LimitedBengaluru560066India SAP(UK)Ltd.MiddlesexTW148HDUK 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2024年第48卷第6期

页      面：1749-1765页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Classification and regression tree process automation rules engine model interpretability explainability model trust 

摘      要：In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise ***,rule-generation has been automated in enterprises,particularly through Machine Learning,to streamline routine ***,these machine models are black boxes where the reasons for the decisions are not always transparent,and the end users need to verify the model proposals as a part of the user acceptance testing to trust *** such scenarios,rules excel over Machine Learning models as the end-users can verify the rules and have more *** many scenarios,the truth label changes frequently thus,it becomes difficult for the Machine Learning model to learn till a considerable amount of data has been accumulated,but with rules,the truth can be *** paper presents a novel framework for generating human-understandable rules using the Classification and Regression Tree(CART)decision tree method,which ensures both optimization and user trust in automated decision-making *** framework generates comprehensible rules in the form of if condition and then predicts class even in domains where noise is *** proposed system transforms enterprise operations by automating the production of human-readable rules from structured data,resulting in increased efficiency and *** the need for human rule construction saves time and money while guaranteeing that users can readily check and trust the automatic judgments of the *** remarkable performance metrics of the framework,which achieve 99.85%accuracy and 96.30%precision,further support its efficiency in translating complex data into comprehensible rules,eventually empowering users and enhancing organizational decision-making processes.