Performance comparison of State-of-the-art Missing Value Imputation Algorithms on Some Bench mark Datasets
This work provides a performance comparison for researchers dealing with missing data in fields like clinical trials, but it is incremental as it reviews existing methods without introducing new ones.
The paper reviewed and compared the performance of recent missing value imputation algorithms on benchmark datasets to address how missing values degrade classifier accuracies, but did not report specific numerical results.
Decision making from data involves identifying a set of attributes that contribute to effective decision making through computational intelligence. The presence of missing values greatly influences the selection of right set of attributes and this renders degradation in classification accuracies of the classifiers. As missing values are quite common in data collection phase during field experiments or clinical trails appropriate handling would improve the classifier performance. In this paper we present a review of recently developed missing value imputation algorithms and compare their performance on some bench mark datasets.