Abstract: Outlier detection of uncertain data can detect objects that are different from most objects from an indeterminate data set.
The distance between objects is measured by the expected semantic distance, and the weighted expectation semantic distance
calculation method is proposed. The attribute weighting fully reflects the different contribution of the attribute in the expected semantic
distance metric, thus improving the application- driven and effective detection of abnormal point detection results. The algorithm
performs abnormal point detection in the classified data set, which can avoid the inaccuracy of the detection result when the normal
abnormal point detection method does not consider the difference between the objects in the database. The experimental results show
that the weighted expected semantic distance anomaly detection method in the classification data overcomes the shortcomings of the
traditional distance metric in the anomaly detection algorithm and optimizes the performance of the algorithm.

Key words: outlier detection, weighted expected semantic distance, uncertain data