A survey on missing data in machine learning

Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Evaluation is performed on the Iris and novel power plant fan data with induced missing values at missingness rate of 5% to 20%.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Missing values occur because of various factors like missing completely at random, missing at random or missing not at random.
• 2 All these may result from system malfunction during data collection or human error during data pre-processing.
• 3 Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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