Issue No. 03 - March (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.254
Der-Chiang Li , National Cheng Kung University, Tainan
Chiao-Wen Liu , National Cheng Kung University, Tainan
Data quantity is the main issue in the small data set problem, because usually insufficient data will not lead to a robust classification performance. How to extract more effective information from a small data set is thus of considerable interest. 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.
Classification, small data set, feature construction, support vector machine.
D. Li and C. Liu, "Extending Attribute Information for Small Data Set Classification," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 452-464, 2010.