Detecting noisy instances with the ensemble filter: A study in software quality estimation
The performance of a classification model is invariably affected by the characteristics of the measurement data it is built upon. If the quality of the data is generally poor, then the classification model will demonstrate poor performance. The detection and removal of noisy instances will improve quality of the data, and consequently, the performance of the classification model. We investigate a noise handling technique that attempts to improve the quality of datasets for classification purposes by eliminating instances that are likely to be noise. Our approach uses twenty five different classification techniques to create an ensemble filter for eliminating likely noise. The basic assumption is that if a given majority of classifiers in the ensemble misclassify an instance, then it is likely to be a noisy instance. Using a relatively large number of base-level classifiers in the ensemble filter facilitates in achieving the desired level of noise removal conservativeness with several possible levels of filtering. It also provides a higher degree of confidence in the noise elimination procedure as the results are less likely to get influenced by (possibly) inappropriate learning bias of a few algorithms with twenty five base-level classifiers than with relatively smaller number of base-level classifiers. Empirical case studies of two high assurance software projects demonstrates the effectiveness of our noise elimination approach by the significant improvement achieved in classification accuracies at various levels of noise filtering.
ensemble filter software quality software metrics noise detection