英语翻译Recently,the class imbalance problem has attracted much

英语翻译
Recently,the class imbalance problem has attracted much attention from researchers in the field of data mining.When learning from imbalanced data in which most examples are labeled as one class and only few belong to another class,traditional data mining approaches do not have a good ability to predict the crucial minority instances.Unfortunately,many real world data sets like health examination,inspection,credit fraud detection,spam identification and text mining all are faced with this situation.In this study,we present a novel model called the ‘‘Information Granulation Based Data Mining Approach” to tackle this problem.The proposed methodology,which imitates the human ability to process information,acquires knowledge from Information Granules rather then from numerical data.This method also introduces a Latent Semantic Indexing based feature extraction tool by using Singular Value Decomposition,to dramatically reduce the data dimensions.In addition,several data sets from the UCI Machine Learning Repository are employed to demonstrate the effectiveness of our method.Experimental results show that our method can significantly increase the ability of classifying imbalanced data.
有道翻译语句不通

最近,类分布不平衡问题已经引起了人们的关注从领域里的研究人员数据挖掘.当学习不平衡数据,多数的例子都被冠以一级,只有几个属于另一个类的数据挖掘方法,传统不佳的预测能力至关重要的少数民族副本.不幸的是,许多真实世界的数据集像健康检查、检验、信用证欺诈检测、垃圾邮件识别和文本挖掘都面临这种状况.在本研究中,我们提出了一种模型就是“信息造粒基础数据挖掘算法”来解决这个问题.该设计方法,其模拟的人才处理信息时,可以获得知识与信息颗粒而不是从数字信息.该方法基于潜在语义标引了特征提取工具,采用奇异值分解,大大降低了其数据维数的增加.此外,不同的数据集该方法利用机器学习库证明了本算法的有效性.实验结果表明,该方法能显著提高分类的不平衡数据的能力.