| This thesis expands upon an existing noise cleansing technique, polishing, enabling it to be used in the Software Quality Prediction domain, as well as any other domain where the data contains continuous values, as opposed to categorical data for which the technique was originally designed. The procedure is applied to a real world dataset with real (as opposed to injected) noise as determined by an expert in the domain. This, in combination with expert assessment of the changes made to the data, provides not only a more realistic dataset than one in which the noise (or even the entire dataset) is artificial, but also a better understanding of whether the procedure is successful in cleansing the data. Lastly, this thesis provides a more in-depth view of the process than previously available, in that it gives results for different parameters and classifier building techniques. This allows the reader to gain a better understanding of the significance of both model generation and parameter selection. |
