Raman spectroscopy is known to have strong potential for providing noninvasive dermatological diagnosis of skin cancer. According to the previous work, various well known methods including maximum a posteriori probability (MAP), multilayer perceptron networks (MLP), and support vector machine (SVM) showed competitive results. Since even the small errors often leads to a fatal result, we investigated the method that reduces classification error perfectly by screening out some ambiguous patterns. Those ambiguous patterns can further be examined by routine biopsy. We incorporated an ambiguous category in MAP, linear classifier using minimum squared error (MSE), and MLP. The experiments involving 216 confocal Raman spectra showed that every methods could perfectly classify basal cell carcinoma (BCC) by screening out some ambiguous patterns. The best results were obtained with MSE. According to the experimental results, MSE gave perfect classification by screening out 8% of test patterns.