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Dealing with incomplete datasets with a confidence attribution algorithm
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AI
DOI：10.1016/j.measurement.2022.111509.png)
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Abstract
En 中文
In this paper, we use multivariate machine learning-based predictors to replace missing data and propose a mechanism to evaluate and track correctness by estimating its confidence level whenever successive missing data points occur. The proposed solution relies on the idea of confidence attribution, which assigns a value to every measurement, indicating how much it is believed to be accurate based on the difference between measured and predicted data. When data is missing, we perform data imputation using the predicted value and estimate confidence. We estimate confidence based solely on parameters used for confidence attribution and information acquired during the predictor's training. We evaluate the solution with two real datasets, one collected from a solar farm and another from a collection of wind turbines. The results show that the accuracy of multivariate models can decrease significantly when input data goes missing, demonstrating the need for the proposed confidence tracking mechanism.
Keywords:
Missing data imputation
Confidence attribution
Error estimation
Stability analysis
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