使用多目标平均单依赖估计量的大数据大模型
Big Models for Big Data using Multi objective averaged one dependence estimators
摘要：
尽管许多研究者试图探索多目标特征选择的各种可能性，但在数据采掘的应用中，它仍然有待于充分发挥其能力，而不是去开发新的应用。本文采用多目标进化算法ENORA对多类分类问题进行特征选择。为了获得一个快速的混合分类器，该分类器可以有效地从大数据中学习，将朴素贝叶斯算法的一种变体--平均n依赖估计量（n=1）与ENORA算法的有效特征选择相融合。该方法旨在解决从完整数据中寻找最优特征子集这一目前仍是一个难题。所获得的分类器的有效性进行了广泛的评估与一系列最流行的21个现实世界的数据集，从小到大。所获得的结果是令人鼓舞的时间，均方根误差，零一损失和分类精度。
英文摘要：
Even though, many researchers tried to explore the various possibilities on multi objective feature selection, still it is yet to be explored with best of its capabilities in data mining applications rather than going for developing new ones. In this paper, multi-objective evolutionary algorithm ENORA is used to select the features in a multi-class classification problem. The fusion of AnDE (averaged n-dependence estimators) with n=1, a variant of naive Bayes with efficient feature selection by ENORA is performed in order to obtain a fast hybrid classifier which can effectively learn from big data. This method aims at solving the problem of finding optimal feature subset from full data which at present still remains to be a difficult problem. The efficacy of the obtained classifier is extensively evaluated with a range of most popular 21 real world dataset, ranging from small to big. The results obtained are encouraging in terms of time, Root mean square error, zero-one loss and classification accuracy.
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。
