标题： 利用计算智能和决策树估计缺失数据 显示英文标题

标题： Estimation of Missing Data Using Computational Intelligence and Decision Trees

摘要： 本文介绍了一种新颖的缺失数据填补范式，该范式结合了决策树与基于自关联神经网络（AANN）模型以及基于主成分分析-神经网络（PCA-NN）模型。 对于每个模型，决策树用于预测遗传算法的搜索范围，以最小化由相应模型衍生出的误差函数。 使用HIV血清流行率数据测试并比较了这些模型填补缺失数据的能力。 结果显示，AANN模型的平均准确率从75.8%提高到86.3%，提高了13%；而PCA-NN模型的平均准确率从66.1%提高到81.6%。 显示英文摘要

摘要： This paper introduces a novel paradigm to impute missing data that combines a decision tree with an auto-associative neural network (AANN) based model and a principal component analysis-neural network (PCA-NN) based model. For each model, the decision tree is used to predict search bounds for a genetic algorithm that minimize an error function derived from the respective model. The models' ability to impute missing data is tested and compared using HIV sero-prevalance data. Results indicate an average increase in accuracy of 13% with the AANN based model's average accuracy increasing from 75.8% to 86.3% while that of the PCA-NN based model increasing from 66.1% to 81.6%.