Robust algorithms for principal component analysis
Pattern Recognition Letters
Data mining: concepts and techniques
Data mining: concepts and techniques
Expert Systems with Applications: An International Journal
Similarity relations and fuzzy orderings
Information Sciences: an International Journal
Principal component analysis of fuzzy data using autoassociative neural networks
IEEE Transactions on Fuzzy Systems
Robust principal component analysis by self-organizing rules based on statistical physics approach
IEEE Transactions on Neural Networks
Nonlinear fuzzy robust PCA algorithms and similarity classifier in bankruptcy analysis
Expert Systems with Applications: An International Journal
User-oriented ontology-based clustering of stored memories
Expert Systems with Applications: An International Journal
Similarity classifier with ordered weighted averaging operators
Expert Systems with Applications: An International Journal
A hybrid intelligent system for medical data classification
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
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.