Adaptive multiple imputations of missing values using the class center
Bibliographic record
Abstract
Abstract Big data has become a core technology to provide innovative solutions in many fields. However, the collected dataset for data analysis in various domains will contain missing values. Missing value imputation is the primary method for resolving problems involving incomplete datasets. Missing attribute values are replaced with values from a selected set of observed data using statistical or machine learning methods. Although machine learning techniques can generate reasonably accurate imputation results, they typically require longer imputation durations than statistical techniques. This study proposes the adaptive multiple imputations of missing values using the class center (AMICC) approach to produce effective imputation results efficiently. AMICC is based on the class center and defines a threshold from the weighted distances between the center and other observed data for the imputation step. Additionally, the distance can be an adaptive nearest neighborhood or the center to estimate the missing values. The experimental results are based on numerical, categorical, and mixed datasets from the University of California Irvine (UCI) Machine Learning Repository with introduced missing values rate from 10 to 50% in 27 datasets. The proposed AMICC approach outperforms the other missing value imputation methods with higher average accuracy at 81.48% which is higher than those of other methods about 9 – 14%. Furthermore, execution time is different from the Mean/Mode method, about seven seconds; moreover, it requires significantly less time for imputation than some machine learning approaches about 10 – 14 s.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".