Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles of Digital Communication and Coding
Principles of Digital Communication and Coding
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Using Error-Correcting Codes for Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Multi-Experts for Touching Digit String Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Computational Statistics & Data Analysis
Troika - An improved stacking schema for classification tasks
Information Sciences: an International Journal
Thinned-ECOC ensemble based on sequential code shrinking
Expert Systems with Applications: An International Journal
A fuzzy intelligent approach to the classification problem in gene expression data analysis
Knowledge-Based Systems
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Ensemble of binary learners for reliable text categorization with a reject option
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Design of reject rules for ECOC classification systems
Pattern Recognition
Enhancing directed binary trees for multi-class classification
Information Sciences: an International Journal
Error-correcting output codes based ensemble feature extraction
Pattern Recognition
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
On the design of an ECOC-Compliant Genetic Algorithm
Pattern Recognition
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This paper presents a new study on a method of designing a multi-class classifier: Data-driven Error Correcting Output Coding (DECOC). DECOC is based on the principle of Error Correcting Output Coding (ECOC), which uses a code matrix to decompose a multi-class problem into multiple binary problems. ECOC for multi-class classification hinges on the design of the code matrix. We propose to explore the distribution of data classes and optimize both the composition and the number of base learners to design an effective and compact code matrix. Two real world applications are studied: (1) the holistic recognition (i.e., recognition without segmentation) of touching handwritten numeral pairs and (2) the classification of cancer tissue types based on microarray gene expression data. The results show that the proposed DECOC is able to deliver competitive accuracy compared with other ECOC methods, using parsimonious base learners than the pairwise coupling (one-vs-one) decomposition scheme. With a rejection scheme defined by a simple robustness measure, high reliabilities of around 98% are achieved in both applications.