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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Attribute weighted Naive Bayes for remote sensing image classification based on cuckoo search algorithm
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Attribute weighted Naive Bayes for remote sensing image classification based on cuckoo search algorithm

机译：基于布谷鸟搜索算法的属性加权朴素贝叶斯遥感图像分类

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摘要

The Naive Bayes classifier(NB) is an effective and simple classification method for remote sensing image classification which is based on probability theory. However, in general, the contribution of each feature is different for classification and its attribute independence assumption is often invalid in the real world. The attribute weighted Naive Bayes(WNB) classifier might have better performance compared to NB, nevertheless, it is a hard and time-consuming work to learn the weight values for all features. Cuckoo search is a newly proposed meta-heuristic optimization algorithm which has been successfully applied for many parameter optimization problems. In the paper, a remote image classification approach is proposed, the attribute weight of which is learnt through cuckoo search algorithm (CSWNB in brief). In order to testify the performance of the proposed method, it is compared to some other evolutionary algorithms, such as attributed weighted Naive Bayes based on Genetic Algorithm (GAWNB), attributed weighted Naive Bayes based on Particle Swarm Optimization (PSOWNB) and attributed weighted Naive Bayes based on Water Wave Optimization (WWOWNB) etc. Experimental results demonstrate that the proposed approach has higher classification accuracy and more stable performance.
机译：朴素贝叶斯分类器是一种基于概率论的有效，简单的遥感图像分类方法。但是，一般而言，每个特征对分类的贡献是不同的，并且其属性独立性假设在现实世界中通常是无效的。与NB相比，属性加权的朴素贝叶斯（WNB）分类器可能具有更好的性能，但是，学习所有功能的权重值是一项艰巨且费时的工作。布谷鸟搜索是一种新提出的元启发式优化算法，已成功应用于许多参数优化问题。本文提出了一种远程图像分类方法，通过布谷鸟搜索算法（简称CSWNB）学习其属性权重。为了验证该方法的性能，将其与其他一些进化算法进行了比较，例如基于遗传算法的属性加权朴素贝叶斯（GAWNB），基于粒子群优化的属性加权朴素贝叶斯（PSOWNB）和属性加权朴素实验结果表明，该方法具有较高的分类精度和较稳定的性能。

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