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The Ripper algorithm is designed to generate rule sets for large datasetswith many features. However, it was shown that the algorithm struggles withclassification performance in the presence of missing data. The algorithmstruggles to classify instances when the quality of the data deteriorates as aresult of increasing missing data. In this paper, a feature selection techniqueis used to help improve the classification performance of the Ripper model.Principal component analysis and evidence automatic relevance determinationtechniques are used to improve the performance. A comparison is done to seewhich technique helps the algorithm improve the most. Training datasets withcompletely observable data were used to construct the model and testingdatasets with missing values were used for measuring accuracy. The resultsshowed that principal component analysis is a better feature selection for theRipper in improving the classification performance.

Improving the performance of the ripper in insurance risk classification : A comparitive study using feature selection