Abstract
In this paper, we propose an improved feature selection method based on genetic algorithms (GA) that selects not only features but also training samples when creating reference patterns. In this method, the chromosome is composed of a set of bits that represents the state of feature selection. The reference pattern in each category is created from the selected training samples, and the discrimination rate for all training samples is used as the fitness value in GA. GA is used as a tool to find the suboptimal combination that increases the discrimination rate from the enormous number of combinations of features and training samples. We perform experiments using five sets of hand-written Kanji character patterns. Each set consists of patterns in two similar categories. The results show that the proposed method, which selects features and training samples, provides higher recognition rates than the conventional method that selects only features.
| Original language | English |
|---|---|
| Pages (from-to) | 650-659 |
| Number of pages | 10 |
| Journal | Journal of the Institute of Image Electronics Engineers of Japan |
| Volume | 40 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2011 |
| Externally published | Yes |
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Keywords
- character recognition
- feature selection
- genetic algorithms
- training sample selection
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Electrical and Electronic Engineering
Cite this
“The Joint Selection of Features and Training Samples using Genetic Algorithms and Its Application to Character Recognition”. / Suzukit, Akira; Morimoto, Masashi; Kimura, Yoshimasa; Shimada, Satoshi; Yonemura, Shunichi.
In: Journal of the Institute of Image Electronics Engineers of Japan, Vol. 40, No. 4, 2011, p. 650-659.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - “The Joint Selection of Features and Training Samples using Genetic Algorithms and Its Application to Character Recognition”
AU - Suzukit, Akira
AU - Morimoto, Masashi
AU - Kimura, Yoshimasa
AU - Shimada, Satoshi
AU - Yonemura, Shunichi
PY - 2011
Y1 - 2011
N2 - In this paper, we propose an improved feature selection method based on genetic algorithms (GA) that selects not only features but also training samples when creating reference patterns. In this method, the chromosome is composed of a set of bits that represents the state of feature selection. The reference pattern in each category is created from the selected training samples, and the discrimination rate for all training samples is used as the fitness value in GA. GA is used as a tool to find the suboptimal combination that increases the discrimination rate from the enormous number of combinations of features and training samples. We perform experiments using five sets of hand-written Kanji character patterns. Each set consists of patterns in two similar categories. The results show that the proposed method, which selects features and training samples, provides higher recognition rates than the conventional method that selects only features.
AB - In this paper, we propose an improved feature selection method based on genetic algorithms (GA) that selects not only features but also training samples when creating reference patterns. In this method, the chromosome is composed of a set of bits that represents the state of feature selection. The reference pattern in each category is created from the selected training samples, and the discrimination rate for all training samples is used as the fitness value in GA. GA is used as a tool to find the suboptimal combination that increases the discrimination rate from the enormous number of combinations of features and training samples. We perform experiments using five sets of hand-written Kanji character patterns. Each set consists of patterns in two similar categories. The results show that the proposed method, which selects features and training samples, provides higher recognition rates than the conventional method that selects only features.
KW - character recognition
KW - feature selection
KW - genetic algorithms
KW - training sample selection
UR - http://www.scopus.com/inward/record.url?scp=85024740467&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85024740467&partnerID=8YFLogxK
U2 - 10.11371/iieej.40.650
DO - 10.11371/iieej.40.650
M3 - Article
VL - 40
SP - 650
EP - 659
JO - Journal of the Institute of Image Electronics Engineers of Japan
JF - Journal of the Institute of Image Electronics Engineers of Japan
SN - 0285-9831
IS - 4
ER -