A multi-classifier training method and classification method based on non-deterministic active learning

Abstract

The invention discloses a multi-classifier training method and classification method based on non-deterministic active learning. The method is: 1) select or initialize a multi-classifier; for each sample in the unmarked sample set, use the multi-classifier to calculate the overall information amount Info of the sample; the overall information amount is: model change information amount and The sum of model tuning information; 2) cluster the unlabeled sample set to obtain J subclasses; 3) select some unlabeled samples with the smallest overall information Info value from each subclass; Select K samples from among them for labeling and add them to the labeled sample set L; 4) Use the updated labeled set L as training data to retrain the multi-classifier; 5) Iteratively execute steps 1) to 4) to set the number of times; The resulting multi-classifier is then used to classify the unlabeled set. The invention realizes the comprehensive evaluation of the amount of sample information, thereby obtaining a highly efficient and intelligent multi-classifier.

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," inProc. 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

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