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Extending Attribute Information for Small Data Set Classification | IEEE Journals & Magazine | IEEE Xplore

Extending Attribute Information for Small Data Set Classification


Abstract:

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...Show More

Abstract:

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.
Published in: IEEE Transactions on Knowledge and Data Engineering ( Volume: 24, Issue: 3, March 2012)
Page(s): 452 - 464
Date of Publication: 30 December 2010

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