重庆理工大学学报2024，Vol.38Issue(9)：162-169,8.DOI:10.3969/j.issn.1674-8425(z).2024.05.021
基于多模型堆叠与特征提取的二打一叫牌算法研究
Bid recommendation model for Fighting the Landlord based on multi-model stacking and feature extraction
刘航 1丁濛 1李淑琴1
作者信息
- 1. 北京信息科技大学 计算机学院, 北京 100101||感知与计算智能联合实验室, 北京 100101
- 折叠
摘要
Abstract
Addressing the granularity limitation observed in existing research on"Fighting the Landlord"bidding decision problem, this paper proposes an approach for training a Bid Recommendation Model.Specifically, a methodology is devised for constructing hand features, including hand vector, hand pattern features, hand tidiness, minimum step in card play, and combination richness.Based on this, we propose a stacked approach to fuse the decision results of four base models and train a meta classifier CatBoost as the final decision model for the bidding decision.Our experimental results indicate that, in comparison with relying solely on hand vector features, this feature construction method significantly enhances model performance.Following the fusion of multiple model decisions through stacking, the accuracy of the second-layer model is further improved, achieving a precision of 84.3854％ on the test set.Moreover, this method provides some references for bidding decision in other card games.关键词
二打一/叫牌算法/计算机博弈/集成学习Key words
Fighting the Landlord/bid algorithm/game algorithm/ensemble learning分类
信息技术与安全科学引用本文复制引用
刘航,丁濛,李淑琴..基于多模型堆叠与特征提取的二打一叫牌算法研究[J].重庆理工大学学报,2024,38(9):162-169,8.
