Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Selection of Image Classifiers
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
The ``Test and Select'' Approach to Ensemble Combination
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Decision Tree Ensemble Creation Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
A dynamic classifier ensemble selection approach for noise data
Information Sciences: an International Journal
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Individual classification models have recently been challenged by ensemble of classifiers, also known as multiple classifier system, which often shows better classification accuracy. In terms of merging the outputs of an ensemble of classifiers, classifier selection has not attracted as much attention as classifier fusion in the past, mainly because of its higher computational burden. In this paper, we propose a novel technique for improving classifier selection. In our method, the simple divide-and-conquer strategy is adapted in that a complex classification problem is divided into simpler binary sub-classification problems. The proposed ensemble classification technique has the following advantages: f) it requires much less computation than the existing ensemble classification methods. 2) It improves overall classification accuracy. 3) It is also suitable for tackling the classification problems which have a relatively large number of target classes. We conduct extensive experiments on a series of multi-class datasets from the UCI (University of California, Irvine) repository and compare several well-known classification approaches. The experimental results demonstrate the advanced performance of the proposed method.