A Sequential Algorithm for Training Text Classifiers

About

The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.

David D. Lewis, William A. Gale · 1994

Related benchmarks

Task	Dataset	Result
Image Classification	CIFAR-100 (test)	Accuracy30	3,518
Image Classification	CIFAR-10 (test)	Accuracy52.6	3,381
Image Classification	FashionMNIST (test)	Accuracy—	461
Image Classification	F-MNIST (test)	Accuracy58.8	173
Image Classification	ImageNet-50 (test)	ASR—	48
Image Classification	ImageNet-10 (test)	Accuracy95.1	42
Domain Discovery	Retrieval Oracle Simulation	Domains Covered6	20
Image Classification	ImageNet 1k (test)	Final Accuracy66.5	12
Image Classification	ImageNet 10/50-class	Accuracy57.9	8
Active Learning for Cascaded Table Extraction	PubTables-1M	AUC71.99	6