Integrating Heterogeneous Classifier Ensembles for EMG Signal Decomposition Based on Classifier Agreement

摘要

In this paper, we present a design methodology for integrating heterogeneous classifier ensembles by employing a diversity-based hybrid classifier fusion approach, whose aggregator module consists of two classifier combiners, to achieve an improved classification performance for motor unit potential classification during electromyographic (EMG) signal decomposition. Following the so-called overproduce and choose strategy to classifier ensemble combination, the developed system allows the construction of a large set of base classifiers, and then automatically chooses subsets of classifiers to form candidate classifier ensembles for each combiner. The system exploits kappa statistic diversity measure to design classifier teams through estimating the level of agreement between base classifier outputs. The pool of base classifiers consists of different kinds of classifiers: the adaptive certainty-based, the adaptive fuzzy $k$ -NN, and the adaptive matched template filter classifiers; and utilizes different types of features. Performance of the developed system was evaluated using real and simulated EMG signals, and was compared with the performance of the constituent base classifiers. Across the EMG signal datasets used, the developed system had better average classification performance overall, especially in terms of reducing classification errors. For simulated signals of varying intensity, the developed system had an average correct classification rate $CC_r$ of 93.8% and an error rate $E_r$ of 2.2% compared to 93.6% and 3.2%, respectively, for the best base classifier in the ensemble. For simulated signals with varying amounts of shape and/or firing pattern variability, the developed system had a $CC_r$ of 89.1% with an $E_r$ of 4.7% compared to 86.3% and 5.6%, respectively, for the best classifier. For real signals, the developed system had a $CC_r$ of 89.4% with an $E_r$ of 3.9% compared to 84.6% and 7.1%, respectively, for the best classifier.