Classification based on fuzzy robust PCA algorithms and similarity classifier

In this article, classification method is proposed where data is first preprocessed using fuzzy robust principal component analysis (FRPCA) algorithms to obtain data in a more feasible form. After this we use similarity classifier for the classification. We tested this procedure for breast cancer data and liver-disorder data. The results were quite promising and better classification accuracy was achieved than using traditional PCA and similarity classifier. Fuzzy robust principal component analysis algorithms seem to have the effect that they project these data sets in a more feasible form, and together with similarity classifier classification on accuracy of 70.25% was achieved with liver-disorder data and 98.19% accuracy was achieved with breast cancer data. Compared to the results achieved with traditional PCA and similarity classifier about 4% higher accuracy was achieved with liver-disorder data and about 0.5% higher accuracy was achieved with breast cancer data.