Machine Learning Based Missing Values Imputation in Categorical Datasets
This study explored the use of machine learning algorithms for predicting and
imputing missing values in categorical datasets. We focused on ensemble models
that use the error correction output codes (ECOC) framework, including
SVM-based and KNN-based ensemble models, as well as an ensemble classifier that
combines SVM, KNN, and MLP models. We applied these algorithms to three
datasets: the CPU dataset, the hypothyroid dataset, and the Breast Cancer
dataset. Our experiments showed that the machine learning algorithms were able
to achieve good performance in predicting and imputing the missing values, with
some variations depending on the specific dataset and missing value pattern.
The ensemble models using the error correction output codes (ECOC) framework
were particularly effective in improving the accuracy and robustness of the
predictions, compared to individual models. However, there are also challenges
and limitations to using deep learning for missing value imputation, including
the need for large amounts of labeled data and the potential for overfitting.
Further research is needed to evaluate the effectiveness and efficiency of deep
learning algorithms for missing value imputation and to develop strategies for
addressing the challenges and limitations that may arise.