当前位置: X-MOL 学术Cybern. Syst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Context Quality Impact in Context-Aware Data Mining for Predicting Soil Moisture
Cybernetics and Systems ( IF 1.7 ) Pub Date : 2020-07-29 , DOI: 10.1080/01969722.2020.1798642
Anca Avram 1 , Oliviu Matei 1 , Camelia-M. Pintea 1 , Petrica C. Pop 1
Affiliation  

Abstract Nowadays research has shown that including context-awareness, in a classic data mining (CDM) process can improve the overall results. The current work investigates the impact of context completeness and accuracy over predictive forecasting for soil moisture in a context-aware data mining (CADM) system. Experiments with different levels of noise and missing data in the context were performed using several machine learning algorithms for both CDM and CADM scenarios. The results show that the soil moisture prediction results are improved when using CADM, even if the quality standards are not completely met.

中文翻译:

用于预测土壤水分的上下文感知数据挖掘中的上下文质量影响

摘要 如今的研究表明,在经典的数据挖掘(CDM)过程中包括上下文感知可以提高整体结果。当前的工作调查了上下文完整性和准确性对上下文感知数据挖掘 (CADM) 系统中土壤水分预测预测的影响。使用针对 CDM 和 CADM 场景的多种机器学习算法,对上下文中不同级别的噪声和缺失数据进行了实验。结果表明,即使没有完全满足质量标准,使用 CADM 也提高了土壤水分预测结果。
更新日期:2020-07-29
down
wechat
bug