Issue No.03 - March (2012 vol.24)
Der-Chiang Li , National Cheng Kung University, Tainan
Chiao-Wen Liu , National Cheng Kung University, Tainan
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.254
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.
Der-Chiang Li, Chiao-Wen Liu, "Extending Attribute Information for Small Data Set Classification", IEEE Transactions on Knowledge & Data Engineering, vol.24, no. 3, pp. 452-464, March 2012, doi:10.1109/TKDE.2010.254