Particle swarm optimization and neural network application for QSAR
Particle swarm optimization and neural network application for QSAR
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DOI：
10.1109/ipdps.2004.1303214
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发表时间：
2004-04
期刊：
影响因子：
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通讯作者：
Zhiwei Wang-;G. L. Durst;R. Eberhart;D. B. Boyd;Zina Ben-Miled
中科院分区：
文献类型：
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作者：
Zhiwei Wang-;G. L. Durst;R. Eberhart;D. B. Boyd;Zina Ben-Miled
仅提供摘要形式。其他研究人员提出了一种成功的建立QSAR模型的方法。它在第一阶段使用二进制粒子群优化（BPSO）进行特征选择，在第二阶段使用反向传播神经网络根据第一阶段选择的特征生成QSAR模型。我们首先在大量的数据集上重建这种方法的结果。然后提出了一种新的方法，解决了反向传播的局限性。该方法在第二阶段使用粒子群优化（PSO）进行训练和引导聚合（装袋），以克服PSO的不稳定性。所提出的方法产生强大的QSAR模型，同时减少由于选择的反向传播参数的变化。
Summary form only given. A successful approach to building QSAR models was proposed by other researchers. It uses binary particle swarm optimization (BPSO) for feature selection in the first stage, and a back propagation neural network in the second stage to generate a QSAR model based on the features selected in the first stage. We start by reestablishing the results of this approach on an extended number of data sets. A new method is then proposed that addresses the limitation of back propagation. This approach uses particle swarm optimization (PSO) in the second stage for training and bootstrap aggregation (bagging) in order to overcome the instability of PSO. The proposed approach yields robust QSAR models, while reducing the variability due to the choice of the back propagation parameters.