Random classification method and device based on combined classifier

Abstract

The invention relates to the technical field of classification calculation, in particular to a random classification method and device based on a combined classifier. The method comprises the following steps that: randomly selecting N pieces of classifiers of which the types are not identical as a combined classifier; selecting a training set and a test set for each classifier which is randomly selected; independently training and testing each classifier to obtain the average accuracy of the combined classifier; according to the average accuracy of the combined classifier, judging whether an elimination mechanism is triggered or not; on the basis of a judgment result, entering a classification calculation step to obtain the classification result of each classifier; and voting for the classification result of each classifier to obtain a final classification result. By use of the method, the phenomena of overfitting and underfitting can be reduced, a discrete variable and a continuous variable can be supported, and a concept drift phenomenon can be overcome.

Description

technical field [0001] The invention relates to the technical field of classification calculations, in particular to a random classification method and device based on a combined classifier. Background technique [0002] Classification has always been one of the hot issues studied in both academia and industry. An accurate and fast classifier can provide great value for enterprises. For example, by accurately categorizing customers, it is possible to target advertising content that customers are interested in and greatly increase the revenue of advertising. [0003] The simplest and most direct way to solve the classification problem is the rule strategy. The so-called rule strategy is to summarize human experience into rules one by one, and then solve practical problems according to these rules and strategies. For example: According to age and gender, customers can be divided into those who are interested in cars and those who are not interested in cars, such as 20-50 fo...

AI Technical Summary

Problems solved by technology

Although discrete variables and continuous variables can be converted into each other according to certain rules, the results of the conversion are often unsatisfactory and complicated.

[0011] 3. Single feature: different classification algorithms have their own different design intentions, and produce different effects in different scenarios

In practical applications, the characteristics and distribution of the training set are different, and may change over time, that is, the phenomenon of concept drift.

However, a single classification algorithm is often unable to cope with this change, so the effect will have a greater impact

[0012] In addition, in the design of these algorithms, the training set data must be accessed repeatedly

Therefore, processing of streaming data cannot be supported

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