Multi-classifier training method and classifying method based on non-deterministic active learning

Abstract

The invention discloses a multi-classifier training method and a classifying method based on non-deterministic active learning. The method comprises the following steps: 1) selecting or initializing a multi-classifier and calculating the overall information quantity info of each sample in an unlabeled sample set by the utilization of the multi-classifier, wherein the overall information quantity is the sum of model change information quantity and model tuning information quantity; 2) clustering the unlabeled sample set to obtain J subclasses; 3) selecting a plurality of unlabelled samples with minimum overall information quantity Info from each subclass; selecting, labeling and adding K samples from the selected sample into a labeled sample set L; 4) training the multi-classifier again through taking the updated labeled set L as training data; 5) iteratively executing the steps 1)-4) to set the number of times and then classifying the unlabelled set by the utilization of the finally-obtained multi-classifier. According to the multi-classifier training method and the classifying method, the comprehensive evaluation on the sample information quantity is realized, so that the efficient and intelligent classifier is obtained.

Description

technical field [0001] The invention relates to a multi-classifier training method and classification method based on non-deterministic active learning, belonging to the technical field of software engineering. Background technique [0002] Data classification has always been a research hotspot, such as patent ZL 201010166225.6 "an adaptive cascade classifier training method based on online learning", patent ZL 200910076428.3 "a cross-domain text sentiment classifier training method and classification method" , Patent ZL 200810094208.9 "Document Classifier Generation Method and System". [0003] In the classification problem of massive data, "active learning" (reference: McCallum and K.Nigam, "Employing EM in pool-based active learning for text classification," in Proc. of the 15th International Conference on Machine Learning, 1998, pp.350–358.) is a machine learning method that efficiently utilizes expert labeling. Its main idea is: the machine actively and targetedly sele...

AI Technical Summary

Problems solved by technology

The traditional multi-classification method based on deterministic active learning only focuses on model tuning and ignores model changes, so it is only suitable for application scenarios where the number of categories is known; while the number of categories is uncertain, the multi-classification method based on deterministic active learning The method is limited to the evaluation of the amount of sample information under the existing N classification model, but it cannot accurately describe and fit the real distribution of the sample data, so that it cannot effectively improve the classification performance

Images