文章基本信息

标题：An Empirical Analysis of Imbalanced Data Classification
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作者：Shu Zhang ; Samira Sadaoui ; Malek Mouhoub 等
期刊名称：Computer and Information Science
印刷版ISSN：1913-8989
电子版ISSN：1913-8997
出版年度：2015
卷号：8
期号：1
页码：151
DOI：10.5539/cis.v8n1p151
出版社：Canadian Center of Science and Education
摘要：SVM has been given top consideration for addressing the challenging problem of data imbalance learning. Here,we conduct an empirical classification analysis of new UCI datasets that have dierent imbalance ratios, sizes andcomplexities. The experimentation consists of comparing the classification results of SVM with two other popularclassifiers, Naive Bayes and decision tree C4.5, to explore their pros and cons. To make the comparative exper-iments more comprehensive and have a better idea about the learning performance of each classifier, we employin total four performance metrics: Sensitive, Specificity, G-means and time-based eciency. For each benchmarkdataset, we perform an empirical search of the learning model through numerous training of the three classifiersunder dierent parameter settings and performance measurements. This paper exposes the most significant resultsi.e. the highest performance achieved by each classifier for each dataset. In summary, SVM outperforms the othertwo classifiers in terms of Sensitive (or Specificity) for all the datasets, and is more accurate in terms of G-meanswhen classifying large datasets.