Extending Attribute Information for Small Data Set Classification
| Autor: | Der-Chiang Li, Chiao-Wen Liu |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Computer science
Feature vector Feature extraction Fuzzy set Similarity measure computer.software_genre Kernel-independent component analysis Kernel principal component analysis Kernel (linear algebra) symbols.namesake Gaussian function Gaussian process Small data Artificial neural network business.industry Pattern recognition Computer Science Applications Data set Support vector machine Statistical classification Computational Theory and Mathematics Principal component analysis symbols Data mining Artificial intelligence business computer Information Systems |
| Zdroj: | IEEE Transactions on Knowledge and Data Engineering. 24:452-464 |
| ISSN: | 1041-4347 |
| DOI: | 10.1109/tkde.2010.254 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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