Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Machine Learning
Limiting the Number of Trees in Random Forests
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Stochastic Organization of Output Codes in Multiclass Learning Problems
Neural Computation
Data-driven decomposition for multi-class classification
Pattern Recognition
An incremental node embedding technique for error correcting output codes
Pattern Recognition
Adaptive mixtures of local experts
Neural Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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
| Hi-index | 12.05 |
Error-correcting output coding (ECOC) is a strategy to create classifier ensembles which reduces a multi-class problem into some binary sub-problems. A key issue in designing any ECOC classifier refers to defining optimal codematrix having maximum discrimination power and minimum number of columns. This paper proposes a heuristic method for application-dependent design of optimal ECOC matrix based on a thinning algorithm. The main idea of the proposed Thinned-ECOC method is to successively remove some redundant and unnecessary columns of any initial codematrix based on a metric defined for each column. As a result, computational cost of the ensemble is reduced while preserving its accuracy. Proposed method has been validated using the UCI machine learning database and further applied to a couple of real-world pattern recognition problems (the face recognition and gene expression based cancer classification). Experimental results emphasize the robustness of Thinned-ECOC in comparison with existing state-of-the-art code generation methods.