数据集分类可用性评估的置信区间方法

如何有效评价训练数据集的可用性,一直是困扰智能分类系统应用的难点问题。针对机器学习领域的数据分类问题,提出了一种基于区间分析和信息粒化的数据集分类可用性的评估方法,用于评价数据集的可分程度。该方法将待评估的数据集定义为分类信息系统,提出了分类置信区间的概念,通过区间分析进行信息粒化。在此信息粒化策略下,定义分类可用性的数学模型,并进一步给出单个属性以及整体数据集的分类可用性的计算方法。选择18个UCI标准数据集作为评估对象,给出了部分数据集分类可用性的评估结果,并且选取3种分类器对所选数据集进行分类实验,最终通过对上述实验结果的分析证明了该评估方法的有效性和可行性。

Confidence Interval Method for Classification Usability Evaluation of Data Sets

It is always a difficult problem to evaluate the usability of training data sets effectively,which hinders the application of intelligent classification systems.Aiming at the issue of data classification in the field of machine learning,based on interval analysis and information granulation,this paper proposed an evaluation method of data classification usability to measure the separability of data sets .In this method,dataset is defined as the classification information system,and the concept of classification confidence interval is put forward,then the information granulation is carried out by interval analysis.Under this information granulation strategy,this paper defined the mathematical model of classification usability,and further gave the calculation method of the classification usability for single attribute and the total data set.In this paper,18 UCI standard data sets were selected as evaluation objects,the evaluation results of classification usability were given,and 3 classifiers were selected to classify the above data sets.Finally,the effectiveness and feasibility of this evaluation method are verified by the analysis of experimental results.