A New Nonlinear Fuzzy Robust PCA Algorithm and Similarity Classifier in Classification of Medical Data Sets
In this article a classification method is proposed where data is first preprocessed using new nonlinear fuzzy robust principal component analysis (NFRPCA) algorithm to get data into more feasible form. After this preprocessing step the similarity classifier is then used for the actual classification. The procedure was tested for dermatology, hepatitis and liver-disorder data. Results were quite promising and better classification accuracy was achieved than using classical PCA and similarity classifier. This new nonlinear fuzzy robust principal component analysis algorithm seems to have the effect that it project the data sets into a more feasible form and when used together with the similarity classifier a classification accuracy of 72.27 %, was achieved with liver-disorder data, 88.94 % with hepatitis, and 97.09 % accuracy was achieved with dermatology data. Compared to results with classical PCA and the similarity classifier, higher accuracies were achieved with the approach using nonlinear fuzzy robust principal component analysis and the similarity classifier.
nonlinear fuzzy robust pca similarity classifier medical data dimension reduction