基于互信息和最速上升策略的特征选择智能算法
Feature selection intelligent algorithm with mutual information and steepest ascent strategy
摘要：
遥感是一种为数据采掘应用提供知识的高级技术。原则上，高光谱图像是一种遥感工具，可提供精确的区域分类。HSI包含超过一百张地面实况（GT）地图的图像。有些图像携带了相关信息，但另一些图像描述了冗余信息，或者它们受到大气噪声的影响。目的是降低HSI的维数。许多研究使用互信息（MI）或MI的归一化形式来选择适当的频带。在本文中，我们设计了一个算法的基础上，也MI，我们结合MI和最速上升算法，以改进的对称不确定性系数为基础的策略来选择相关波段的分类HSI。该算法是一种特征选择工具和包装策略。我们对HSI AVIRIS 92 AV 3C进行了研究。这是一个控制冗余的人工智能系统;我们必须明确结果算法和人类决策的差异，这可以被视为人类决策可能与智能算法不同的案例研究。高光谱图像、分类、特征选择、互信息、Redundance、最陡上升。人工智能
英文摘要：
Remote sensing is a higher technology to produce knowledge for data mining applications. In principle hyperspectral images (HSIs) is a remote sensing tool that provides precise classification of regions. The HSI contains more than a hundred of images of the ground truth (GT) map. Some images are carrying relevant information, but others describe redundant information, or they are affected by atmospheric noise. The aim is to reduce dimensionality of HSI. Many studies use mutual information (MI) or normalised forms of MI to select appropriate bands. In this paper we design an algorithm based also on MI, and we combine MI with steepest ascent algorithm, to improve a symmetric uncertainty coefficient-based strategy to select relevant bands for classification of HSI. This algorithm is a feature selection tool and a wrapper strategy. We perform our study on HSI AVIRIS 92AV3C. This is an artificial intelligent system to control redundancy; we had to clear the difference of the result's algorithm and the human decision, and this can be viewed as case study which human decision is perhaps different to an intelligent algorithm. Index Terms - Hyperspectral images, Classification, Fea-ture selection, Mutual Information, Redundancy, Steepest Ascent. Artificial Intelligence
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