Indian Journal of Science and Technology
DOI: 10.17485/ijst/2016/v9i32/83088
Year: 2016, Volume: 9, Issue: 32, Pages: 1-6
Original Article
Vijayakumar Kuppusamy* and Ilango Paramasivam
School of Computer Science and Engineering, VIT University, Vellore - 632014, Tamil Nadu, India; [email protected]
[email protected]
*Author for correspondence
Vijayakumar Kuppusamy
School of Computer Science and Engineering
Email:[email protected]
Objectives: In this study, MissForest algorithm is used to analyze the impact of missing data. Statistical Analysis: For categorical dataset, MissForest package is used to impute various missing values and tested with missing data incrementally, first with 5% missing attributes of the records from the original dataset then with 10% and so on. Findings: The Proportion of Falsely Classified (PFC) is computed for categorical dataset. Since the good performance of MissForest leads to a value close to zero and bad performance to a value around one, the performance measured for the Nursery dataset is quite good. Application: This approach has good effect when the ratio of missing data is low. Missforest algorithm can be used for numerical or categorical data. The results are improved for continuous data.
Keywords: Categorical Data, Data Mining, Imputation, Missing Data, MissForest
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