Comparison of machine learning algorithms for classification of Penaeid prawn species

机译：机器学习算法对虾对虾分类的比较

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摘要

Classification of Penaeid prawn species is an important research problem in the area of aquaculture. In literature, many ML algorithms have been proposed for classification. In this paper, performance and usability of penaied prawn species are compared using K-Nearest Neighbourhood (KNN) algorithm, Support Vector Machines (SVM) and Back Propagation Neural Networks (BPNN). For experimental evaluation a dataset containing 100 samples of each species are classified. Also the classification accuracy of each species is analyzed using the above mentioned three classifiers. Experimental results indicate that the SVM out performs KNN classifier and ANN classifiers and may potentially fill gap for the current use or for future.

机译：对虾对虾种类的分类是水产养殖领域的重要研究问题。在文献中，已经提出了许多机器学习算法用于分类。在本文中，使用K-最近邻（KNN）算法，支持向量机（SVM）和反向传播神经网络（BPNN）比较了对虾的性能和可用性。为了进行实验评估，对包含每种物种的100个样本的数据集进行了分类。另外，使用上述三个分类器分析每种物种的分类精度。实验结果表明，SVM可以执行KNN分类器和ANN分类器，并有可能填补当前或未来的空白。

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来源
《International Conference on Computing for Sustainable Global Development》|2016年|1610-1613|共4页
会议地点 New Delhi(IN)
作者
V. Sucharita; S. Jyothi; P. Venkateswara Rao;
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作者单位

K.L. University Vaddeswaram India;

Sri Padmavati Women's University Tirupati India;

ASCET Gudur India;

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Handheld computers; Decision support systems; Support vector machines;

机译：手提电脑；决策支持系统；支持向量机;

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Comparison of machine learning algorithms for classification of Penaeid prawn species

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