Isolated forest-based binary classification abnormal point detection method and information data processing terminal

Abstract

The invention belongs to the technical field of communication control and communication processing, and discloses an isolated forest-based binary classification abnormal point detection method and aninformation data processing terminal. The method comprises the steps of carrying out initial static average blocking on an original data set, and calculating the density in the block and the mean density; after calculating the density in each block of the static block, reducing the data set by taking the mean density of the original data set as a threshold value; constructing an isolated forest byusing a node recursion method; performing corresponding feature extraction and datamation on the original data set, and calculating the spatial position distances between the clustering center pointand other points; adding the abnormal score calculated on the basis of the density and the distance and the abnormal score calculated on the basis of the proof information and comparing with a corresponding threshold value. According to the method, the accuracy of an abnormal point detection algorithm is effectively improved, the actual data size in the abnormal detection process can be greatly reduced, the calculation resources are saved, and the abnormal detection efficiency is improved, and the robustness of an abnormal detection algorithm is enhanced.

Description

technical field [0001] The invention belongs to the technical field of communication control and communication processing, and in particular relates to a method for detecting abnormal points based on isolated forest binary classification and an information data processing terminal. Background technique [0002] At present, the closest existing technology: Among the commonly used outlier detection algorithms, there are many classic algorithms, which cut into anomaly detection from different angles, and the anomaly detection of a class of support vector machines based on neural networks uses points and points The calculation of the space Euclidean distance between can get the minimum interval, so as to determine the corresponding support vector, and then maximize the distance between the two support vectors through the objective function under the constraints, so that the separation hyperplane can be determined to achieve Purpose of anomaly detection. Of course, the above met...

AI Technical Summary

Problems solved by technology

[0023] (1) The existing technology does not sparsely express the original data set, resulting in redundant detection of normal data points in the process of anomaly detection

[0024] (2) The existing technology does not have a better treatment for the abnormal points on the surface where the edge point and the two subspaces intersect, resulting in the addition of normal data to the abnormal data set, and many data will produce fuzzy operations, lacking more specific and strict anomaly detection criteria; not very robust

[0025] (3) The traditional anomaly detection algorithm does not label the data

In the face of non-linearly separable data information, direct classification will cause some data at the boundary to be lost, and the completeness of the data set will be lost.

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