| dc.description.abstract | Many real-world datasets contain missing values, affecting the efficiency of many
classification algorithms. However, this is an unavoidable error due to many reasons such as
network problems, physical devices, etc. Some classification algorithms cannot work properly
with incomplete datasets. Therefore, it is crucial to handle missing values. Imputation methods
have proven their effectiveness in handling missing data, hence, significantly improve
classification accuracy. There are two types of imputation methods. Both have their pros and
cons. Single imputation can lead to low accuracy while multiple imputations are timeconsuming. One high-accuracy algorithm proposed in this thesis is called “Classification based
on Association Rules” (CARs). CARs has been proven to yield higher accuracy compared to
others. However, there is no investigation on how to mine CARs with incomplete datasets. This
thesis aims to develop an efficient imputationmethod for mining CARs on incomplete datasets.
To show the impact of each imputation method, two types of imputation will be applied and
compared in experiments. | en_US |
