Classification with big data has bec

Classification with big data has become one of the latest trends when talking about learning from the available information.
The data growth in the last years has rocketed the interest in effectively acquiring knowledge to analyze and predict trends. The
variety and veracity that are related to big data introduce a degree of uncertainty that has to be handled in addition to the vol-
ume and velocity requirements. This data usually also presents what is known as the problem of classification with imbalanced
datasets, a class distribution where the most important concepts to be learned are presented by a negligible number of examples in
relation to the number of examples from the other classes. In order to adequately deal with imbalanced big data we propose the
Chi-FRBCS-BigDataCS algorithm, a fuzzy rule based classification system that is able to deal with the uncertainly that is intro-
duced in large volumes of data without disregarding the learning in the underrepresented class. The method uses the MapReduce
framework to distribute the computational operations of the fuzzy model while it includes cost-sensitive learning techniques in its
design to address the imbalance that is present in the data. The good performance of this approach is supported by the experimental
analysis that is carried out over twenty-four imbalanced big data cases of study. The results obtained show that the proposal is able
to handle these problems obtaining competitive results both in the classification performance of the model and the time needed for
the computation

結果 (中文) 1:

在处理大量数据来已成为最新的趋势之一在谈到学习从现有的资料时。最后一年的数据增长已经一飞冲天的有效获取知识，分析和预测趋势的兴趣。的品种和相关于大数据的准确性引入了一定程度的不确定性，除了卷-处理ume 和速度的要求。此数据通常还提出了所谓的来与不平衡的问题在那里，最重要的概念，将学会提出有极少数的例子类分布的数据集从其他类的实例数的关系。为了适当地处理我们提出的不平衡大数据志-FRBCS-BigDataCS 算法，模糊规则基于按系统能够处理不确定性就是介绍 — —在大量的数据，而不忽视任职人数偏低的班里学习了。该方法使用 MapReduce分布时的模糊模型的计算操作的框架包括代价敏感学习技巧在其设计，以处理在数据中存在的不平衡。这种方法的良好性能支持由实验进行超过二十四不平衡大数据案例的分析。结果表明这项建议是能来处理这些问题，获得竞争结果无论是在按性能的模型和为所需的时间计算

結果 (中文) 3:

fi阳离子分类大数据已经成为一个最新的趋势，当谈到从可用的信息的学习。在过去的几年中增长
数据的猛增，有效地获取知识来分析和预测趋势的兴趣。该品种和准确性
都涉及到大数据引入一定程度的不确定性，必须另外的卷柄
时间和速度的要求。这个数据通常还提出了所谓的不平衡数据集分类fi阳离子
问题，一类分布在哪里可以学到的最重要的概念是由一个可以忽略不计的从其他类
实例之间的数量关系的例子。为了充分应对不平衡大数据提出
赤FRBCS bigdatacs算法，基于规则的模糊分类fi阳离子系统能够处理不确定，介绍
了大量数据没有不顾学习弱势阶级。该方法使用MapReduce框架分发
模糊模型的计算操作，它包括在其
代价敏感学习方法设计来解决不平衡数据中存在的。这种方法的良好性能的实验
分析，进行了超过二十四的不平衡情况的研究，支持大数据。结果表明，该方案可以处理这些问题
获取竞争结果在模型的分类性能和所需阳离子fi
时间计算