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Missing data filling algorithm in OA (office automation) cooperative office system

The current algorithms can only effectively fill in fewer missing data when filling out missing data in office automation (OA) cooperative office system, and the filling accuracy is worse when the missing data is large. In order to solve this problem, a missing data filling algorithm based on improved Mahalanobis distance is proposed. It used a top-down recursion to process raw data, got the training set of the observation sample and the associated class labels, and generated readable rules and decision trees. It optimizes the generated decision tree by using the genetic algorithm of the elite strategy, and classifies the missing data through the optimized decision tree. According to the classification results combined with the entropy calculation method in the improved Mahalanobis distance algorithm, there is a single decreasing relationship between entropy size and Mahalanobis distance of missing data, which can reduce the impact on the predictive value of related data, and achieve the missing data filling in OA cooperative office system. The experimental results showed that the proposed algorithm can effectively improve the accuracy of data filling, and the accuracy of data filling can be stable over 80% under different missing rates.