基于计算智能和决策树的缺失数据估计
Estimation of Missing Data Using Computational Intelligence and Decision Trees
摘要：
本文介绍了一种新的范式来填补缺失数据，结合决策树与自联想神经网络（AANN）为基础的模型和主成分分析-神经网络（PCA-NN）为基础的模型。对于每个模型，决策树用于预测遗传算法的搜索边界，该搜索边界最小化从相应模型导出的误差函数。使用HIV血清流行率数据测试和比较模型插补缺失数据的能力。结果表明，基于AANN的模型的平均准确度从75.8%提高到86.3%，而基于PCA-NN的模型的平均准确度从66.1%提高到81.6%，平均准确度提高了13%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
