A New Evolutionary Fuzzy Instance-Based Learning Approach: Application for Detection of Parkinson's Disease

In this paper, we present a new evolutionary instance-based learning method by integrating the bacterial foraging optimization (BFO) technique with fuzzy k-nearest neighbor classifier (FKNN), termed as BFO-FKNN. In the proposed approach, the issue of parameter tuning problem in FKNN is tackled using the BFO technique. The effectiveness of the proposed method has been rigorously evaluated against the Parkinson’s disease (PD) diagnosis problem. The simulation results have shown that the proposed approach outperforms the other two counterparts via 10-fold cross validation analysis. In addition, compared to the existing methods in previous studies, the proposed method can also be regarded as a promising success with the excellent classification accuracy of 96.39%.

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