The Impact of Cross-Validation Methods on the Performance Estimates of Classification Algorithms
碩士 === 國立成功大學 === 資訊管理研究所 === 106 === Cross validation is a popular approach for evaluating the performance of classification algorithms. The variance of the accuracy estimate resulting from k-fold cross validation is generally relatively large, and several evaluated methods are therefore developed...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/uj4ny9 |
| Summary: | 碩士 === 國立成功大學 === 資訊管理研究所 === 106 === Cross validation is a popular approach for evaluating the performance of classification algorithms. The variance of the accuracy estimate resulting from k-fold cross validation is generally relatively large, and several evaluated methods are therefore developed for reducing the variance. When a data set is processed by two evaluation methods for the same classification algorithm, the resulting accuracies for the two evaluation methods may not be independence for performance comparison. The purpose of this research is to propose statistical methods for comparing the performance for various evaluation methods. When a data set is classified by the same algorithm, the independence test for two binary random variables is first introduced to identify whether the predictions of the same instance for two evaluation methods are independent or not. Then statistical methods are proposed to comparing the performance of a classification algorithm on single data set or multiple data sets processed by two dependent evaluation methods. Classification algorithms decision tree induction and k-nearest neighbor are chosen to test the performance of four evaluation methods. The experimental results of the independence test on twenty ordinary data sets shows that the predictions of instances for various evaluation methods are generally dependent, and the testing results of our statistical methods suggested that the accuracy estimates resulting from various evaluation methods are not significantly different. Nonparametric statistical methods are employed to test the variance of accuracy for ordinary data sets and the mean and variance of F-measure for imbalanced data sets. Those tests also indicate that the performance of a classification algorithm will not be significantly different when data sets are processed by various evaluation methods.
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