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- Title
Selective Feature Generation Method for Classification of Low-dimensional Data.
- Authors
Choi, S. -I.; Choi, S. T.; Yoo, H.
- Abstract
We propose a method that generates input features to effectively classify low-dimensional data. To do this, we first generate high-order terms for the input features of the original low-dimensional data to form a candidate set of new input features. Then, the discrimination power of the candidate input features is quantitatively evaluated by calculating the 'discrimination distance' for each candidate feature. As a result, only candidates with a large amount of discriminative information are selected to create a new input feature vector and the discriminant features that are to be used as input to the classifier are extracted from the new input feature vectors by using a subspace discriminant analysis. Experiments on low-dimensional data sets in the UCI machine learning repository and several kinds of low-resolution facial image data show that the proposed method improves the classification performance of low-dimensional data by generating features.
- Subjects
FEATURE extraction; DISCRIMINANT analysis; BIG data; MACHINE learning; DIMENSIONAL reduction algorithms
- Publication
International Journal of Computers, Communications & Control, 2018, Vol 13, Issue 1, p24
- ISSN
1841-9836
- Publication type
Article
- DOI
10.15837/ijccc.2018.1.2931