An approach based on Bayesian Networks to fill missing values
To take full advantage of all information available, it is best to use as many available catalogs as possible. For example, adding u-band or X-ray information while classifying quasars based on their variability is highly likely to improve the overall performance. Because these catalogs are taken with different instruments, bandwidths, locations, times, etc., the intersection of these catalogs is smaller than any single catalog; thus, the resulting multi-catalog contains missing values. Traditional classification methods cannot deal with the resulting missing data problem because to train a classification model it is necessary to have all features for all training members. PyMissingData allows you to predict missing values given the observed data and dependency relationships between variables.