Fast structural SVM text classification optimization algorithm

Abstract

The invention provides a fast structural SVM text classification optimization algorithm. For a text classification task of an unbalanced data set, the algorithm directly optimizes the performance evaluation index of the main category by using the performance evaluation method such as on an accuracy rate, a recall rate, an AUC, and the like. The method is different from most conventional text classification algorithms that: instead of learning a single rule to predict a tag of a single sample, the method formalizes the learning problem into a multiple predictive problem on all the samples in the data set, and is different from idea in the conventional method that the reduction of overall classification error rate is taken as the target, so that classification accuracy of the text data set under the unbalanced condition is improved, and the classification performance is effectively improved; and referring to a Structural SVM based sparse approximation algorithm, with better time complexity, the method can be used for evaluation indexes such as the F value calculated from the accuracy rate and the recall rate, and the optimization of the AUC, so that the time complexity is reduced and better results are obtained.

Description

technical field [0001] The invention relates to the field of text classification, and more specifically, relates to a fast structured support vector machine text classification optimization algorithm. Background technique [0002] With the rapid development of Internet technology, the classification, organization and management of massive data has become a topic of great research significance. Among these data, text data is the largest category, including news reports, user comments, emails, advertisements, and so on. These communication carriers containing a large amount of text content require a complete classification mechanism to manage and filter information, so text classification came into being. Text classification refers to the classification of texts into pre-defined categories by computer according to some automatic classification algorithm based on the content of the text, which is an important topic in the fields of information storage and information retrieval...

AI Technical Summary

Problems solved by technology

[0007] Third, although F value and AUC can be used as good evaluation indicators, you can consider training a text classification model by directly optimizing F value or AUC, which is similar to Ranking SVM and other methods to a certain extent, and they aim to reduce training errors. , but the performance is poor when the training samples are unbalanced, and the classifier does not distinguish well from the training instances generated by the small class samples, or even turns a blind eye

But not all integration methods are effective. Even if the F value and AUC can be improved, the combination of multiple classifiers leads to extremely complex models, high time complexity, and poor explainability.

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