Random forest-based self-training learning system and method for anti-addiction system
A random forest and learning method technology, applied in the field of self-training learning system based on random forest, can solve the problem of few anti-addiction systems, achieve strong universal applicability and accurate selection
Active Publication Date: 2019-01-29
XIAOVO TECH +1
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Problems solved by technology
At present, there is still little research work on anti-addiction systems, and this field is still in the early stages of development.
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[0042] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, in the case of no conflict, the following embodiments and features in the embodiments can be combined with each other.
[0043] see Figure 1 to Figure 5. It should be noted that the structures, proportions, sizes, etc. shown in the drawings attached to this specification are only used to match the content disclosed in the specification, for those who are familiar with this technology to understand and read, an...
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The invention provides a random forest-based self-training learning system and method for an anti-addiction system. The method comprises the following steps: PCA training is performed on at least onemarked game feature sequence to obtain a game feature sequence training set; a classifier based on a random forest identifies an unlabeled game feature sequence, and adds the unlabeled game feature sequence with the highest confidence to the game feature sequence training set; re-performing PCA training on the data of the game feature sequence training set until a preset number of cycles is reached or the game feature sequence training set is not increased; the input test game feature sequence is identified using the game feature sequence training set. The self-learning method based on randomforest provided by the invention is used for solving the problem that a large amount of game sequence data in an anti-indulgence system is unmarked, and a better classifier is jointly constructed by utilizing a large amount of unmarked game sequence data and a small amount of marked game sequence data.
Description
technical field [0001] The invention relates to the technical field of machine learning, in particular to a random forest-based self-training learning system and method for an anti-addiction system. Background technique [0002] The game anti-addiction system has been in operation for a full ten years since 2007. In August 2005, the General Administration of Press and Publication issued the "Development Standards for Online Game Anti-addiction System", requiring seven large domestic online game operating companies to prepare for the development of an anti-addiction system. In September 2005, the online game anti-addiction system was successively installed and tested in the products of major online game companies. In March 2006, the General Administration of Press and Publication issued the "Notice on the Implementation of the Anti-addiction System of Online Games to Protect the Physical and Mental Health of Minors", and decided to implement the anti-addiction system of onli...
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IPC IPC(8): G06K9/62G06N99/00
CPCG06F18/285G06F18/2135G06F18/214
Inventor 骆源徐彬方品应臣浩
Owner XIAOVO TECH