This paper proposes a novel variable selection method involving the use of a newly defined metric called the partial Kullback-Leibler (KL) information measure to evaluate the contribution of each variable (dimension) in the data. In this method, the probability density functions of recorded data are estimated through a multidimensional probabilistic neural network trained on the basis of KL information theory. The partial KL information measure is then defined as the ratio of the values before and after dimension elimination in the data. The effective dimensions for classification can be selected eliminating ineffective ones based on the partial KL information in a one-by-one manner. In the experiments, the proposed method was applied to channel selection with nine subjects (including an amputee), and effective channels were selected from all channels attached to each subject's forearm. The results showed that the number of channels was reduced by 54.3 ±19.1%, and the average classification rate for evaluation data using selected three or four channels was 96.6 ±2.8% (min: 92.1%, max: 100%). These outcomes indicate that the proposed method can be used to select effective channels (optimal or quasi-optimal) for accurate classification.
ASJC Scopus subject areas
- Biomedical Engineering