Text classification method based on improved firefly algorithm and K neighbors

Abstract

The invention discloses a text classification method based on an improved firefly algorithm and K neighbors. A text feature selection model is constructed by combining information gain and the fireflyalgorithm. The method comprises the following steps: all features are sorted by using information gain, and then a more representative feature subset on a feature set sorted in the front is found out by using the relatively strong optimization capability of an improved firefly algorithm. The step length factor alpha in the firefly algorithm is adjusted, so that the global search capability of the algorithm is ensured, and the local search capability is also ensured. A new fitness function is introduced, so that the dimensionality of the features is properly reduced on the basis of improvingthe precision of the feature subsets. And finally, the model is used for text feature selection, and the obtained feature subset is used for KNN text classification. According to the method, the defects that a firefly algorithm is prone to early maturing and falling into local optimum, the convergence speed is low and the like in the process of searching for the optimal text feature subset can bewell overcome, so that a more accurate subset is obtained, and the text classification accuracy is improved.

Description

technical field [0001] The invention belongs to the field of Chinese text classification, and specifically refers to a text classification method based on the improved firefly algorithm and K-nearest neighbors. Background technique [0002] With the rapid development of Internet technology, more and more users can not only conveniently obtain information resources on the Internet, but also publish information on the Internet, that is, users are the carriers of information release and reception at the same time. Although the representation of information is becoming more and more abundant, so far, the main representation of information is still text. Faced with such a large amount of text data, it is difficult for people to find the information they are interested in. If we only rely on traditional manual methods to organize and manage these text data, it will not only consume a lot of material and manpower, but also difficult to achieve. This forces people to look for a ne...

AI Technical Summary

Problems solved by technology

However, most researchers mainly focus on improving the accuracy of feature subsets, without much consideration of the inherent defects of this type of algorithm: easy to fall into local optimum prematurely, complex calculation, slow convergence, etc.

As a result, the optimal solution search efficiency is low, which affects the convergence accuracy and speed of the algorithm.

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