Evaluating the performance of artificial neural networks for the classification of freshwater benthic macroinvertebrates
Evaluating the performance of artificial neural networks for the classification of freshwater benthic macroinvertebrates
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DOI：
10.1016/j.ecoinf.2014.01.004
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发表时间：
2014-03-01
影响因子：
5.1
通讯作者：
Juhola, Martti
中科院分区：
文献类型：
--
作者：
Joutsijoki, Henry;Meissner, Kristian;Juhola, Martti
大型无脊椎动物是水生生态系统的重要功能组成部分。它们指示各种类型的人为压力的能力得到广泛承认，这使它们成为淡水生物监测的一个组成部分。在生物监测中使用大型无脊椎动物依赖于人工分类鉴定，目前这是一个由训练有素的分类专家进行的耗时和成本密集的过程。大型无脊椎动物的自动类群识别是一个相对较新的研究进展以前的研究已经显示出巨大的潜力，这个要求苛刻的数据挖掘应用的解决方案。在这项研究中，我们收集了1350幅图像，从8个不同的大型无脊椎动物类群，其目的是研究人工神经网络（ANN）的自动分类识别的大型无脊椎动物的适用性。更具体地说，重点是绘制不同的训练算法的多层感知器（MLP），概率神经网络（PNN）和径向基函数网络（RBFN）。我们进行了彻底的实验测试，我们总共测试了13个训练算法的MLP。最好的分类精度的MLP，95.3%，得到了两个共轭梯度反向传播变化和缩放共轭梯度反向传播。PNN和RBFN的准确率分别为92.8%和95.7%。结果表明，如何正确选择人工神经网络是非常重要的，以获得高精度的大型无脊椎动物的自动分类单元识别和所获得的模型可以优于识别的水平，这是由分类学家。(C)2014爱思唯尔有限公司版权所有。
Macroinvertebrates form an important functional component of aquatic ecosystems. Their ability to indicate various types of anthropogenic stressors is widely recognized which has made them an integral component of freshwater biomonitoring. The use of macroinvertebrates in biomonitoring is dependent on manual taxa identification which is currently a time-consuming and cost-intensive process conducted by highly trained taxonomical experts. Automated taxa identification of macroinvertebrates is a relatively recent research development Previous studies have displayed great potential for solutions to this demanding data mining application. In this research we have a collection of 1350 images from eight different macroinvertebrate taxa and the aim is to examine the suitability of artificial neural networks (ANNs) for automated taxa identification of macroinvertebrates. More specifically, the focus is drawn on different training algorithms of Multi-Layer Perceptron (MLP), probabilistic neural network (PNN) and Radial Basis Function network (RBFN). We performed thorough experimental tests and we tested altogether 13 training algorithms for MLPs. The best classification accuracy of MLPs, 95.3%, was obtained by two conjugate gradient backpropagation variations and scaled conjugate gradient backpropagation. For PNN 92.8% and for RBFN 95.7% accuracies were achieved. The results show how important a proper choice of ANN is in order to obtain high accuracy in the automated taxa identification of macroinvertebrates and the obtained model can outperform the level of identification which is made by a taxonomist. (C) 2014 Elsevier B.V. All rights reserved.