计算机技术与发展2024，Vol.34Issue(9)：159-166,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0156
基于RTOPSIS的集成学习的综合评价研究
Research on Comprehensive Evaluation of Ensemble Learning Based on RTOPSIS
摘要
Abstract
Stacking ensemble learning is often considered a"black-box"model,utilizing predictions from multiple base learners as input and employing a meta-learner to generate the final prediction.This complexity makes it difficult to understand exactly how each base learner contributes to the final result.To address this issue,we propose the RTOPSIS method.This method combines grey relational co-efficient calculations with the TOPSIS method,providing decision-makers with an effective tool to clearly reveal the contribution of each base learner in the Stacking model to the final outcome.Specifically,the RTOPSIS algorithm is employed as a substitute for traditional discriminative methods,comprehensively considering the relationships between base learners and meta-learners to yield more objective and rational model ranking results;The grey relational analysis algorithm is applied to calculate the weights of individual base learners in the Stacking model and reflect their contributions to the final outcome.Experimental results demonstrate that in the comprehensive evaluation of Stacking models,relative to singular metrics such as Accuracy,AUC,and F1-score,the RTOPSIS algorithm provides more appropriate rankings for the six models considered in this study,and is largely consistent with the ranking results of the classical distance-based TOPSIS algorithm.Therefore,the proposed RTOPSIS algorithm exhibits a more comprehensive evaluation effect in Stacking model assessment.关键词
优劣解距离/灰色关联度/权重/Stacking模型/综合评价Key words
technique for order preference by similarity to an ideal solution/grey correlation degree/weight/Stacking model/comprehensive evaluation criteria分类
信息技术与安全科学引用本文复制引用
左胜勇,冯立超,陈学斌,张春艳..基于RTOPSIS的集成学习的综合评价研究[J].计算机技术与发展,2024,34(9):159-166,8.基金项目
国家自然科学基金区域创新发展联合基金项目(U20A20179) （U20A20179）
