Learning belief regression trees from evidential data

机译：从证据数据学习信仰回归树

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Regression trees are extended to be learnt from data with epistemic uncertainty. Modelling uncertainty with belief functions, the attribute selection strategy based on error interval is discussed and a complete tree construction procedure is proposed. As a general approach, error intervals weighted by mass functions are calculated for making the best splitting choice. Including classical regression trees as a special case, belief regression trees can deal with various kinds of uncertain data such as imprecise, uncertain and noisy ones.

机译：延长回归树从认知不确定性与数据中的数据学习。讨论了基于误差间隔的属性选择策略建模不确定性，提出了一种完整的树施工过程。作为一种通用方法，计算由质量函数加权的误差间隔，用于制作最佳分割选择。包括古典回归树作为一个特例，信仰回归树可以处理各种不确定的数据，如不精确，不确定和嘈杂的数据。

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来源
《Youth Academic Annual Conference of Chinese Association of Automation》|2017年|638p|共6页
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作者
Liyao Ma; Ziyi Li; Yong Wang;
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中图分类 TP2-53;
关键词
Regression tree analysis; Training; Uncertainty; Biological system modeling; Predictive models; Data mining; Data models;

机译：回归树分析;培训;不确定性;生物系统建模;预测模型;数据挖掘;数据模型;

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4. Learning belief regression trees from evidential data [C] . Liyao Ma, Ziyi Li, Yong Wang Youth Academic Annual Conference of Chinese Association of Automation . 2017

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5. Profiling student use of calculators in the learning of high school mathematics through Classification and Regression Trees [D] . Crowe, Cheryll Elizabeth 2008

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6. Evidential Decision Tree Based on Belief Entropy [O] . Mujin Li, Honghui Xu, Yong Deng 2019

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7. Online active learning of decision trees with evidential data [O] . Ma, Liyao, Destercke, Sebastien, Wang, Yong 2016

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Learning belief regression trees from evidential data

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