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首页> 外文会议>International Conference on Soft Computing and Intelligent Systems;International Symposium on Advanced Intelligent Systems >Robust interval support vector interval regression networks for interval-valued data with outliers
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Robust interval support vector interval regression networks for interval-valued data with outliers

机译：具有异常值的区间值数据的鲁棒区间支持向量区间回归网络

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摘要

Recently, the interval-valued data analysis is popular research topic in symbolic data analysis (SDA). For some of applications, it is natural to use interval-valued data because of uncertainty existing in the measurements, variability for defining a term (the minimum and the maximum temperature during a day), extremely behavior description (maximum wind speed in a given country), etc. However, the obtained data are always subject to outliers in some of applications. Moreover, the outliers may occur due to various reasons, such as erroneous measurements or noisy data from the tail of noise distribution functions. In order to handle the interval-valued data with outliers, a novel approach, called the robust interval support vector interval regression networks (RISVIRNs), is proposed. The RISVIRNs is extended from our previous work (e.g. the support vector interval regression networks; SVIRNs). It is easy to find that SVIRNs can have interval-valued data for outputs, but only can deal with the crisp input. Moreover, the outlier's effects are not discussed in SVIRNs. Hence, the support vector regression with intervalvalued for input data (SVRI2) approach is proposed to determine the initial structure of RISVIRNs and to remove outliers form the interval-valued data set. Due to such approach can provide a better initial structure of RISVIRNs, the proposed approach can have fast convergent speed and robust against outliers. The experimental results with real data sets show the validity of the proposed RISVIRNs.
机译：最近，区间值数据分析是符号数据分析（SDA）中的热门研究主题。在某些应用中，自然会使用间隔值数据，因为测量中存在不确定性，定义术语的可变性（一天中的最低和最高温度），极端的行为描述（给定国家/地区的最大风速） ）等。但是，在某些应用程序中，获得的数据始终会受到异常值的影响。此外，离群值可能由于各种原因而发生，例如错误的测量结果或来自噪声分布函数尾部的嘈杂数据。为了用离群值处理区间值数据，提出了一种新的方法，称为鲁棒区间支持向量区间回归网络（RISVIRNs）。 RISVIRN是我们先前工作的扩展（例如，支持向量区间回归网络; SVIRN）。很容易发现SVIRN可以具有间隔值数据作为输出，但是只能处理清晰的输入。此外，在SVIRN中未讨论异常值的影响。因此，提出了针对输入数据采用间隔值支持向量回归（SVRI2）的方法，以确定RISVIRN的初始结构并从间隔值数据集中去除离群值。由于这种方法可以提供更好的RISVIRNs初始结构，因此所提出的方法可以具有快速的收敛速度和对异常值的鲁棒性。具有真实数据集的实验结果表明了所提出的RISVIRN的有效性。

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