Classification in sample by sample process, a classifier is requested to combine information across multiple samples drawn from the same data source, the results are combined using a strategy such as majority are selected. To solve the problem of classification failure, a new hazard function in multisample classification is introduced ie Multi-aggregative factored K-NN Classifier. This method evaluates the classification of multisampling problems, such as electromyographic (EMG) data, by making aggregate features available to a per-sample classifier. It is found that the accuracy of this approach is superior to that of traditional methods such as majority selection for this problem. The classification improvements of this method, in conjunction with a confidence measure expressing the per-sample probability of classification failure (i.e., a hazard function) is described and measured. This paper compares the existing method Bayesian and the proposed Multi-aggregative factored KNN approach. The experimental results displayed a prominent improvement by using the proposed algorithm.
EMG, Motor Unit Action Potential, Additional Feature Sets, Classifier
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