Selective Feature Generation Method for Classification of Low-dimensional Data
- Authors
- Choi, S. -I.; Choi, S. T.; Yoo, H.
- Issue Date
- Feb-2018
- Publisher
- CCC PUBL-AGORA UNIV
- Keywords
- feature generation; input feature selection; feature extraction; discriminant distance; low-dimensional data; data classification
- Citation
- INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, v.13, no.1, pp 24 - 38
- Pages
- 15
- Journal Title
- INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
- Volume
- 13
- Number
- 1
- Start Page
- 24
- End Page
- 38
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/1260
- DOI
- 10.15837/ijccc.2018.1.2931
- ISSN
- 1841-9836
1841-9844
- 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.
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Collections - College of Medicine > College of Medicine > 1. Journal Articles
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