Trading MIPS and memory for knowledge engineering
Communications of the ACM
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Making large-scale support vector machine learning practical
Advances in kernel methods
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
A study of thresholding strategies for text categorization
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 support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Learning to rank for hybrid recommendation
Proceedings of the 21st ACM international conference on Information and knowledge management
Text classification with relatively small positive documents and unlabeled data
Proceedings of the 21st ACM international conference on Information and knowledge management
Scoring-Thresholding pattern based text classifier
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Learning to rank from structures in hierarchical text classification
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Recursive regularization for large-scale classification with hierarchical and graphical dependencies
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A pattern based two-stage text classifier
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Effective learning in multi-label classification (MLC) requires an appropriate level of abstraction for representing the relationship between each instance and multiple categories. Current MLC methods have been focused on learning-to-map from instances to ranked lists of categories in a relatively high-dimensional space. The fine-grained features in such a space may not be sufficiently expressive for characterizing discriminative patterns, and worse, make the model complexity unnecessarily high. This paper proposes an alternative approach by transforming conventional representations of instances and categories into a relatively small set of link-based meta-level features, and leveraging successful learning-to-rank retrieval algorithms (e.g., SVM-MAP) over this reduced feature space. Controlled experiments on multiple benchmark datasets show strong empirical evidence for the strength of the proposed approach, as it significantly outperformed several state-of-the-art methods, including Rank-SVM, ML-kNN and IBLR-ML (Instance-based Logistic Regression for Multi-label Classification) in most cases.