This paper proposes a new attribute construction approach which converts the original data attributes into a higher dimensional feature space to extract more attribute information by a similarity-based algorithm using the classification-oriented fuzzy membership function. Seven data sets with different attribute sizes are employed to examine the performance of the proposed method. The results show that the proposed method has a superior classification performance when compared to principal component analysis (PCA), kernel principal component analysis (KPCA), and kernel independent component analysis (KICA) with a Gaussian kernel in the support vector machine (SVM) classifier.