Context-sensitive learning methods for text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Learning routing queries in a query zone
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Active learning using adaptive resampling
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Maximum likelihood estimation for filtering thresholds
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A refinement approach to handling model misfit in text categorization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature selection using linear classifier weights: interaction with classification models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive sampling for thresholding in document filtering and classification
Information Processing and Management: an International Journal
Incremental mining of information interest for personalized web scanning
Information Systems
Spam decisions on gray e-mail using personalized ontologies
Proceedings of the 2009 ACM symposium on Applied Computing
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Information is often represented in text form and classified into categories for efficient browsing, retrieval, and dissemination. Unfortunately, automatic classifiers may conduct many misclassifications. One of the reasons is that the documents for training the classifiers are mainly from the categories, leading the classifiers to derive category profiles for distinguishing each category from others, rather than measuring the extent to which a document's content overlaps that of a category. To tackle the problem, we present a technique DP4FC to help various classifiers to improve the mining of category profiles. Upon receiving a document, DP4FC helps to create dynamic category profiles with respect to the document, and accordingly helps to make proper filtering and classification decisions. Theoretical analysis and empirical results show that DP4FC may make a classifier's performance both better and more stable.