Preprocessing and imputing method for structural data

    公开(公告)号:US11841839B1

    公开(公告)日:2023-12-12

    申请号:US18143059

    申请日:2023-05-03

    Applicant: ZHEJIANG LAB

    CPC classification number: G06F16/215 G06N3/0455 G06N3/0475

    Abstract: The present invention discloses a preprocessing and imputing method for structural data, comprising: step 1, querying the missing information of an original data, counting missing values, and obtaining a missing rate for the original data; step 2, based on the missing rate, performing listwise deletion on the original data, and then traversing the rows to generate corresponding dichotomous arrays, converting the arrays to the form of histogram, calculating the maximum rectangular area formed by the corresponding histogram, and then sorting all rectangular areas to obtain the maximum complete information matrix; step 3, using multiple imputation by chained equations, auto-encoders, or generative adversarial imputation networks to impute missing values on the original data. The present invention can carry out missing information statistics on the original data, automatically search the maximum complete information that meets the conditions, impute the structural data, greatly improve the quality of the original dataset and convenience for subsequent prediction tasks.

    Method and device for detecting and correcting abnormal scoring of peer reviews

    公开(公告)号:US11989167B1

    公开(公告)日:2024-05-21

    申请号:US18489879

    申请日:2023-10-19

    Applicant: ZHEJIANG LAB

    CPC classification number: G06F16/215 G06F16/2246

    Abstract: The present application disclose a method and a device for detecting and correcting abnormal scoring of peer reviews, which includes: converting collected scoring data into a two-dimensional matrix and preprocessing the data; determining the anomaly of the processed structured data with a one-way anomaly detection method, a consistency check method and a two-way anomaly detection method, and classifying the detected abnormal data into an abnormal data set; repairing the abnormal data for the abnormal data set with an information entropy correction method; generating an ability evaluation report according to the abnormal data set, performing weighed averaging on the corrected scoring data according to the scoring weights of reviewers in the ability evaluation report to obtain a final scoring result, and generating an abnormal scoring correction report. The present application can effectively detect the abnormal phenomenon of peer reviews in the performance appraisal of enterprise personnel.

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