C4.5: programs for machine learning
C4.5: programs for machine learning
On the boosting ability of top-down decision tree learning algorithms
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Multiclass Alternating Decision Trees
ECML '02 Proceedings of the 13th European Conference on Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Option Decision Trees with Majority Votes
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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
Multi-label Lazy Associative Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
INDUCTION FROM MULTI-LABEL EXAMPLES IN INFORMATION RETRIEVAL SYSTEMS: A CASE STUDY
Applied Artificial Intelligence
Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
ART-Based Neural Networks for Multi-label Classification
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Multi-label learning by instance differentiation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-automatic dynamic auxiliary-tag-aided image annotation
Pattern Recognition
An Enhanced Probabilistic Neural Network Approach Applied to Text Classification
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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
Multi-label learning by exploiting label dependency
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Gait-based human age estimation
IEEE Transactions on Information Forensics and Security
Multilabel classification using error correction codes
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Designing a multi-label kernel machine with two-objective optimization
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
A multilabel text classification algorithm for labeling risk factors in SEC form 10-K
ACM Transactions on Management Information Systems (TMIS)
Two stage architecture for multi-label learning
Pattern Recognition
FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors
Expert Systems with Applications: An International Journal
Multi-instance multi-label learning
Artificial Intelligence
Age estimation using multi-label learning
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
An efficient multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
Linear methods for reduction from ranking to multilabel classification
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
An extensive experimental comparison of methods for multi-label learning
Pattern Recognition
Improving multi-label classifiers via label reduction with association rules
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Label-to-region with continuity-biased bi-layer sparsity priors
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Fast multi-label core vector machine
Pattern Recognition
MCut: a thresholding strategy for multi-label classification
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Multi-label classification by exploiting label correlations
Expert Systems with Applications: An International Journal
Fundamenta Informaticae - Concurrency, Specification and Programming
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Multi-label decision procedures are the target of the supervised learning algorithm we propose in this paper. Multi-label decision procedures map examples to a finite set of labels. Our learning algorithm extends Schapire and Singer's Adaboost.MH and produces sets of rules that can be viewed as trees like Alternating Decision Trees (invented by Freund and Mason). Experiments show that we take advantage of both performance and readability using boosting techniques as well as tree representations of large set of rules. Moreover, a key feature of our algorithm is the ability to handle heterogenous input data: discrete and continuous values and text data.