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Machine Learning-Driven Classification for Enhanced Rule Proposal Framework

Computer Systems Science & Engineering 2024年第6期48卷 1749-1765页

作者： B.Gomathi R.Manimegalai Srivatsan Santhanam Atreya Biswas Department of Computer Science and Engineering PSG Institute of Technology and Applied ResearchCoimbatore641062India SAP Labs India Private Limited Bengaluru560066India SAP(UK)Ltd. MiddlesexTW148HDUK

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.

关键词： Classification and regression tree process automation rules engine model interpretability explainability model trust

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