基于改进的C4.5决策树的自动售货机决策支持系统的构建和优化
Ping Li1, Fang Xiong1, Xibei Huang1
1School of Information and Mechatronic Engineering, Hunan International Economics University, Changsha, 410205, China.
Heliyon
|February 6, 2024
概括
本研究开发了一种使用机器学习的自动售货机的决策支持系统 (DSS). 它通过人工智能驱动的产品分类和销售预测来提高运营效率和客户体验.
科学领域:
- * 人工智能 * 人工智能
- * 机器学习 * 机器学习
- * 运营管理 运营管理
背景情况:
- * 随着市场竞争的加剧,制造商需要精细化运营管理.
- *机器学习 (ML) 是人工智能 (AI) 的一个分支,在各种系统中提供了重要的应用前景.
- *自动售货机为应用人工智能提高运营效率提供了一个实际案例.
研究的目的:
- *使用决策树算法为自动售货机构建产品分类模型.
- * 为使用神经网络 (NN) 的自动售货机构建销售预测模型.
- * 开发用于自动售货机的决策支持系统 (DSS) 的理论框架.
主要方法:
- *使用决策树算法 (C4.5和改进的C4.5) 进行产品分类.
- * 逆向传播神经网络 (BPNN) 用于销售预测.
- *采用强化学习来优化系统性能,并适应市场变化.
主要成果:
- *C4.5算法的准确率在68%到87%之间.
- * 改进的C4.5算法表现出可比的准确性 (67%-87%),数据集运行时间显著减少.
- * BPNN的销售预测显示出高准确度,预测数据曲线与实际数据密切匹配.
结论:
- * 开发的DSS框架,整合了ML模型,增强了自动售货机的操作.
- *这项研究证明了人工智能在提高效率和客户满意度方面的有效性.
- * 强化学习集成有望在动态市场环境中提高系统智能和适应性.
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