A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Applying Boosting to Similarity Literals for Time Series Classification
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Enhanced 1-NN time series classification using badness of records
Proceedings of the 2nd international conference on Ubiquitous information management and communication
The 1¢ Recognizer: a fast, accurate, and easy-to-implement handwritten gesture recognition technique
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
An approach to dimensionality reduction in time series
Information Sciences: an International Journal
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In previous work, we showed that the use of Multiple Input Representation(MIR) for the classification of time series data provides complementary information that leads to better accuracy. [4]. In this paper, we introduce the Static Minimization-Maximization approach to build Multiple Classifier Systems(MCSs) using MIR. SMM consists of two steps. In the minimization step, a greedy algorithm is employed to iteratively select the classifiers from the knowledge space to minimize the training error of MCSs. In the maximization step, a modified version of Behavior Knowledge Space(BKS), Balanced Behavior Knowledge Space(BBKS), is used to maximize the expected accuracy of the whole system given that the training error is minimized. Several popular techniques including AdaBoost, Bagging and Random Subspace are used as the benchmark to evaluate the proposed approach on four time series data sets. The results obtained from our experiments show that the performance of the proposed approach is effective as well as robust for the classification of time series data. In addition, this approach could be further extended to other applications in our future research.