Algebraic feature extraction of image for recognition
Pattern Recognition
Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
A Comparative Study of Classification Based Personal E-mail Filtering
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Theoretical Computer Science
Email classification with co-training
CASCON '01 Proceedings of the 2001 conference of the Centre for Advanced Studies on Collaborative research
A Framework for Adaptive Mail Classification
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
A Neural Network Based Approach to Automated E-Mail Classification
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Bias Analysis in Text Classification for Highly Skewed Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A review of feature selection techniques in bioinformatics
Bioinformatics
Text classification based on multi-word with support vector machine
Knowledge-Based Systems
Feature selection in bankruptcy prediction
Knowledge-Based Systems
An adaptive personalized news dissemination system
Journal of Intelligent Information Systems
Adaptive Bayesian network classifiers
Intelligent Data Analysis
Issues in evaluation of stream learning algorithms
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
An adaptive prequential learning framework for bayesian network classifiers
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
On the utility of incremental feature selection for the classification of textual data streams
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
An efficient parzen-window based network intrusion detector using a pattern synthesis technique
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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Email foldering is a challenging problem mainly due to its high dimensionality and dynamic nature. This work presents ABC-DynF, an adaptive learning framework with dynamic feature space that we use to compare several incremental and adaptive strategies to cope with these two difficulties. Several studies have been carried out using datasets from the ENRON email corpus and different configuration settings of the framework. The main aim is to study how feature ranking methods, concept drift monitoring, adaptive strategies and the implementation of a dynamic feature space can affect the performance of Bayesian email classification systems.