The nature of statistical learning theory
The nature of statistical learning theory
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
A maximal figure-of-merit learning approach to text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
ACM Transactions on Information Systems (TOIS)
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Combining Subclassifiers in Text Categorization: A DST-Based Solution and a Case Study
IEEE Transactions on Knowledge and Data Engineering
Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Multi-label learning by instance differentiation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Multi-modal multi-label semantic indexing of images based on hybrid ensemble learning
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Selecting representative and distinctive descriptors for efficient landmark recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Mining the “Voice of the Customer” for Business Prioritization
ACM Transactions on Intelligent Systems and Technology (TIST)
Refinement method of post-processing and training for improvement of automated text classification
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Explicit performance metric optimization for fusion-based video retrieval
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Multi-Label Classification Method for Multimedia Tagging
International Journal of Multimedia Data Engineering & Management
Multimedia event detection with multimodal feature fusion and temporal concept localization
Machine Vision and Applications
Journal of Signal Processing Systems
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We propose a multiclass (MC) classification approach to text categorization (TC). To fully take advantage of both positive and negative training examples, a maximal figure-of-merit (MFoM) learning algorithm is introduced to train high performance MC classifiers. In contrast to conventional binary classification, the proposed MC scheme assigns a uniform score function to each category for each given test sample, and thus the classical Bayes decision rules can now be applied. Since all the MC MFoM classifiers are simultaneously trained, we expect them to be more robust and work better than the binary MFoM classifiers, which are trained separately and are known to give the best TC performance. Experimental results on the Reuters-21578 TC task indicate that the MC MFoM classifiers achieve a micro-averaging F1 value of 0.377, which is significantly better than 0.138, obtained with the binary MFoM classifiers, for the categories with less than 4 training samples. Furthermore, for all 90 categories, most with large training sizes, the MC MFoM classifiers give a micro-averaging F1 value of 0.888, better than 0.884, obtained with the binary MFoM classifiers.