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感应不经意决策树的增强型任意时间算法

Enhanced Anytime Algorithm for Induction of Oblivious Decision Trees
课程网址: http://videolectures.net/ecml07_saveliev_eaaio/  
主讲教师: Albina Saveliev
开课单位: 本古里安大学
开课时间: 2008-01-29
课程语种: 英语
中文简介:
高速非平稳数据流的实时数据挖掘在机械车辆高效运行、无线传感器网络、城市交通控制、库存数据分析等领域具有很大的潜力。这些领域的特点是大量的噪声、不确定的数据和有限的资源(主要是计算时间)。在求解质量与计算时间之间进行权衡的任何时候,人工智能技术应用于时间关键问题都是可行的。在本文中,我们提出了一种新的、增强的、用于构建分类模型的Anytime算法版本,称为信息网络(in)。算法改进的目的是在保证模型质量的同时降低计算成本。采用标准的10倍交叉验证,通过分类精度来评价诱导模型的质量。该算法的改进是在多个enchmark数据流上实现的。
课程简介: Real-time data mining of high-speed and non-stationary data streams has a large potential in such fields as efficient operation of machinery and vehicles, wireless sensor networks, urban traffic control, stock data analysis etc.. These domains are characterized by a great volume of noisy, uncertain data, and restricted amount of resources (mainly computational time). Anytime algorithmsoffer a tradeoff between solution quality and computation time, which has proveduseful in applying artificial intelligence techniques to time-critical problems. Inthis paper we are presenting a new, enhanced version of an anytime algorithm forconstructing a classification model called Information Network (IN). The algorithmimprovement is aimed at reducing its computational cost while preservingthe same level of model quality. The quality of the induced model is evaluatedby its classification accuracy using the standard 10-fold cross validation. Theimprovement in the algorithm anytime performance is demonstrated on severalbenchmark data streams.
关 键 词: 计算机科学; 数据流; 算法
课程来源: 视频讲座网
最后编审: 2019-12-06:lxf
阅读次数: 30