Semi-supervised learning-based method for filtering junk users in social network
A semi-supervised learning and social network technology, applied in network data retrieval, network data indexing, data processing applications, etc., can solve labeling bottlenecks, time-consuming and labor-intensive problems
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[0035]Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar meanings throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.
[0036] figure 1 It is a schematic diagram of the overall process structure of the present invention. As shown in the figure, the present invention provides a method for filtering social network junk users based on semi-supervised learning. First, information gain feature selection is performed on the high-dimensional social network data; then the training sample set is used to train and learn by using the Tri-training algorithm to obtain the optimal classifier; finally, the performance of the classifier is evaluated by using the test sample set. The specific steps are as ...
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