Application of support vector regression to CPUE analysis for southern bluefin tuna Thunnus maccoyii, and its comparison with conventional methods
Application of support vector regression to CPUE analysis for southern bluefin tuna Thunnus maccoyii, and its comparison with conventional methods
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DOI：
10.1007/s12562-014-0770-6
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发表时间：
2014-07
影响因子：
1.9
通讯作者：
H. Shono
中科院分区：
文献类型：
--
作者：
H. Shono
本文以南方蓝鳍金枪鱼的实际渔业数据为例，利用支持向量回归、神经网络和树形回归三种数据挖掘模型和两种常规统计方法(方差分析和广义线性模型)对单位努力渔获量标准化进行了描述。基于均方误差、平均绝对误差和三个相关系数来比较这五个模型的统计性能，三个相关系数是以观测值和相应的预测值之间的差来衡量的。结果表明，支持向量机回归模型的预测效果与神经网络相当(或更好)，且优于树回归模型、方差分析模型和基于CPUE分析的广义线性模型。基于这些数据挖掘模型得到的预测值，我们提出了一种简单的析因分析方法来提取CPUE年趋势。这种方法有望显著降低这些用于数据挖掘的模型估计CPUE年趋势的难度，并因其易用性、通用性和高性能而应用于CPUE分析。
This paper describes the catch per unit effort (CPUE) standardization using three models for data mining (support vector regression, neural network and tree regression model) and two conventional statistical methods (analysis of variance and generalized linear model) using the actual fishery data for southern bluefin tunaThunnus maccoyii. Statistical performances of these five models were compared based on mean square error, mean absolute error and three correlation coefficients, which are measured by the difference between the observed and the corresponding predicted values. As a result, the performance of support vector regression is equivalent to (or better than) that of neural networks, and these two models are superior to the tree regression model, analysis of variance, and generalized linear model based on CPUE analyses of actual fishery data for southern bluefin tuna. We suggest a simple method for factorial analysis to extract the CPUE year trend based on the predicted values obtained from these data mining models. This method is expected to contribute markedly to reduce the difficulty of estimating the CPUE year trends by these models for data mining and should be applied to CPUE analyses because of its ease of use, general versatility and high performance .