TY - GEN
T1 - Comparative results of attribute reduction techniques for thai handwritten recognition with support vector machines
AU - Phienthrakul, Tanasanee
AU - Samnienggam, Massaya
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Data reduction is an important step in machine learning and big data analysis. The handwritten recognition is a problem that uses a lot of data to get the good results. Thus, the attribute reduction can be applied to improve the accuracy of classification and reduces the learning time. In this paper, the attribute reduction techniques are studies. These techniques are applied to the Thai handwritten recognition problems. Support vector machines (SVMs) are used to verify the results of 4 attribute reduction techniques, i.e., principle component analysis (PCA), local discriminant analysis (LDA), locality preserving projection (LPP), and neighborhood preserving embedding (NPE). All of these 4 techniques will transform the original attributes to a new space with the different methods. The results show that LDA is a suitable data reduction technique for classifying the handwritten character with SVM. Only 10 % of features can give the accuracy about 47.68 % for 89 classes of the characters. This technique may give a better result when the suitable feature extraction techniques are applied.
AB - Data reduction is an important step in machine learning and big data analysis. The handwritten recognition is a problem that uses a lot of data to get the good results. Thus, the attribute reduction can be applied to improve the accuracy of classification and reduces the learning time. In this paper, the attribute reduction techniques are studies. These techniques are applied to the Thai handwritten recognition problems. Support vector machines (SVMs) are used to verify the results of 4 attribute reduction techniques, i.e., principle component analysis (PCA), local discriminant analysis (LDA), locality preserving projection (LPP), and neighborhood preserving embedding (NPE). All of these 4 techniques will transform the original attributes to a new space with the different methods. The results show that LDA is a suitable data reduction technique for classifying the handwritten character with SVM. Only 10 % of features can give the accuracy about 47.68 % for 89 classes of the characters. This technique may give a better result when the suitable feature extraction techniques are applied.
KW - Attribute reduction
KW - Handwritten recognition
KW - Local discriminant analysis
KW - Locality preserving projection
KW - Neighborhood preserving embedding
KW - Principal components analysis
KW - Support vector machines
UR - https://www.scopus.com/pages/publications/84976508063
U2 - 10.1007/978-3-319-40415-8_8
DO - 10.1007/978-3-319-40415-8_8
M3 - Conference contribution
AN - SCOPUS:84976508063
SN - 9783319404141
T3 - Advances in Intelligent Systems and Computing
SP - 67
EP - 77
BT - Recent Advances in Information and Communication Technology 2016 - Proceedings of the 12th International Conference on Computing and Information Technology, IC2IT
A2 - Meesad, Phayung
A2 - Boonkrong, Sirapat
A2 - Unger, Herwig
PB - Springer Verlag
T2 - 12th International Conference on Computing and Information Technology, IC2IT 2016
Y2 - 7 July 2016 through 8 July 2016
ER -