A Machine Learning Approach for Estimating Missing Data in Nonstationary Environments

Tinofirei Museba
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引用次数: 1

Abstract

The assumption with most learning techniques and algorithms developed this far is that data is complete and will continuously be available or that data conforms to a stationary distribution. Real world applications are often streaming and their measurements are often sampled after an extended period of time thereby giving rise to the formation of a time series. Measurement devices assigned to measure nonstationary quantities are subject to failure. When a failure occurs, the process of approximating missing values becomes difficulty. The process of approximating missing values in such dynamic environments is further exacerbated by the chaotic and unpredictable nature of the evolving data. Typical examples include stock market, network intrusion detection systems and seismic waves. To estimate missing values with traditional statistical methods can lead to bias when applied to environments that evolve with time. This paper introduces an ensemble of regressors approach to approximate missing data in online nonstationary data. The approach learns new concepts incrementally and the current learnt concept is then used to approximate the missing values.
非平稳环境中缺失数据估计的机器学习方法
到目前为止,大多数学习技术和算法的假设是,数据是完整的,并且将持续可用,或者数据符合平稳分布。现实世界的应用程序通常是流的,它们的测量通常在一段较长的时间后采样,从而产生时间序列的形成。用于测量非平稳量的测量装置容易失效。当故障发生时,逼近缺失值的过程变得困难。在这种动态环境中,逼近缺失值的过程由于不断变化的数据的混沌和不可预测的性质而进一步加剧。典型的例子包括股票市场、网络入侵检测系统和地震波。当应用于随时间变化的环境时,用传统的统计方法估计缺失值可能会导致偏差。本文介绍了在线非平稳数据中缺失数据的回归量集合逼近方法。该方法逐步学习新概念,然后使用当前学习的概念来近似缺失值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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