Review and comparison of methods to study the contribution of variables in artificial neural network models
Review and comparison of methods to study the contribution of variables in artificial neural network models
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DOI：
10.1016/s0304-3800(02)00257-0
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发表时间：
2003-02-15
影响因子：
3.1
通讯作者：
Lek, S
中科院分区：
文献类型：
--
作者：
Gevrey, M;Dimopoulos, L;Lek, S
相信人工神经网络（ANN）模型在生态学中的预测质量，我们已经把我们的兴趣转向他们的解释能力。比较了7种能给出输入因子相对贡献和/或贡献分布的方法：（i）“PaD”法（对于偏导数）方法在于根据输入变量计算输出的偏导数;（i i）“权重”方法是使用连接权重的计算;（iii）“扰动”方法对应于输入变量的扰动;（iv）“轮廓”方法是一个输入变量的连续变化，而其他输入变量保持恒定在固定值;（v）“经典逐步”方法是当添加（向前）或消除时误差值变化的观察。（反向）.对输入变量进行阶跃运算;（vi）“改进的逐步a”使用与经典逐步相同的原理，但是在训练网络时发生输入的消除，对应于所研究的输入变量的连接权重也被消除;（vii）“改进的逐步B”涉及逐步训练和固定网络，一个输入变量处于其平均值以注意对误差的影响。在这项研究中测试的数据涉及使用栖息地特征的褐鳟鱼产卵红的密度的预测。PaD方法被认为是最有用的，因为它给出了最完整的结果，其次是Profile方法，它给出了输入变量的贡献曲线。扰动方法允许对输入参数进行良好的分类，而权重方法已被简化，但这两种方法缺乏稳定性。其次是两种改进的逐步方法（a和B），两者都给出了完全相同的结果，但贡献没有得到充分表达。最后，经典的逐步方法给出了最差的结果。(C)出版社：Elsevier Science B. V.
Convinced by the predictive quality of artificial neural network (ANN) models in ecology, we have turned our interests to their explanatory capacities. Seven methods which can give the relative contribution and/or the contribution profile of the input factors were compared: (i) the 'PaD' (for Partial Derivatives) method consists in a calculation of the partial derivatives of the output according to the input variables; (i i) the 'Weights' method is a computation using the connection weights; (iii) the 'Perturb' method corresponds to a perturbation of the input variables; (iv) the 'Profile' method is a successive variation of one input variable while the others are kept constant at a fixed value; (v) the 'classical stepwise' method is an observation of the change in the error value when an adding (forward) or an elimination (backward).step of the input variables is operated; (vi) 'Improved stepwise a' uses the same principle as the classical stepwise, but the elimination of the input occurs when the network is trained, the connection weights corresponding to the input variable studied is also eliminated; (vii) 'Improved stepwise b' involves the network being trained and fixed step by step, one input variable at its mean value to note the consequences on the error. The data tested in this study concerns the prediction of the density of brown trout spawning redds using habitat characteristics. The PaD method was found to be the most useful as it gave the most complete results, followed by the Profile method that gave the contribution profile of the input variables. The Perturb method allowed a good classification of the input parameters as well as the Weights method that has been simplified but these two methods lack stability. Next came the two improved stepwise methods (a and b) that both gave exactly the same result but the contributions were not sufficiently expressed. Finally, the classical stepwise methods gave the poorest results. (C) 2002 Published by Elsevier Science B.V.