A Novel Classifier and Classification Method Based on Information Gain and Online Support Vector Machine

Abstract

The invention provides a novel classifier based on an information gain and an online support vector machine, and a classification method thereof. In academic researches of recent years, an online support vector classifier is concerned by some scholars in the information filtering field. The classification method of the novel classifier based on the information gain and the online support vector machine comprises the following steps of: step one, pre-treating sample information to obtain characteristics of a sample; step two, calculating an information amount of each characteristic by using an information gain method and selecting the needed characteristic according to a certain strategy; step three, establishing a characteristic vector capable of adapting to an online support vector machine model according to the selected characteristic; step four, training the novel classifier based on the online support vector machine by utilizing an online model; and step five, utilizing the classifier to classify samples. The novel classifier and the classification method, disclosed by the invention, are used for classifying texts and filtering information.

Description

Technical field:

[0001] The invention relates to the field of machine learning and classification technology; in particular, it relates to a new classifier and classification method based on information gain and online support vector machines. Background technique:

[0002] With the massive increase of network resources, network information classification methods are particularly important. At present, the commonly used classification methods include Bayesian method, support vector machine, logistic regression, decision tree, neural network, etc. Among these methods, support vector machines have been shown to outperform many other classification methods. Support Vector Machines (SVMs) is a new pattern recognition method developed on the basis of statistical learning theory. It shows many unique advantages in solving small sample, nonlinear, high-dimensional recognition problems, and can be extended to other machine learning problems such as function fitting. Although there are...

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