  The Ripper algorithm is designed to generate rule sets for large datasets
with many features. However, it was shown that the algorithm struggles with
classification performance in the presence of missing data. The algorithm
struggles to classify instances when the quality of the data deteriorates as a
result of increasing missing data. In this paper, a feature selection technique
is used to help improve the classification performance of the Ripper model.
Principal component analysis and evidence automatic relevance determination
techniques are used to improve the performance. A comparison is done to see
which technique helps the algorithm improve the most. Training datasets with
completely observable data were used to construct the model and testing
datasets with missing values were used for measuring accuracy. The results
showed that principal component analysis is a better feature selection for the
Ripper in improving the classification performance.
