TY - GEN
T1 - Integrated feature preprocessing for classification based on neural incremental attribute learning
AU - Wang, Ting
AU - Zhou, Wei
AU - Zhu, Xiaoyan
AU - Liu, Fangzhou
AU - Guan, Sheng Uei
N1 - Publisher Copyright:
© 2016 ISIF.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - Incremental Attribute Learning (IAL) is a feasible machine learning strategy for solving high-dimensional pattern classification problems. It gradually trains features one by one, which is quite different from those conventional machine learning approaches where features are trained in one batch. Preprocessing, such as feature selection, feature ordering and feature extraction, has been verified as useful steps for improving classification performance by previous IAL studies. However, in the previous research, these preprocessing approaches were individually employed and they have not been applied for training simultaneously. Therefore, it is still unknown whether the classification results can be further improved by these different preprocess approaches when they are used at the same time. This study integrates different feature preprocessing steps for IAL, where feature extraction, feature selection and feature ordering are simultaneously employed. Experimental results indicate that such an integrated preprocessing approach is applicable for pattern classification performance improvement. Moreover, statistical significance testing also verified that such an integrated preprocessing approach is more suitable for the datasets with high-dimensional inputs.
AB - Incremental Attribute Learning (IAL) is a feasible machine learning strategy for solving high-dimensional pattern classification problems. It gradually trains features one by one, which is quite different from those conventional machine learning approaches where features are trained in one batch. Preprocessing, such as feature selection, feature ordering and feature extraction, has been verified as useful steps for improving classification performance by previous IAL studies. However, in the previous research, these preprocessing approaches were individually employed and they have not been applied for training simultaneously. Therefore, it is still unknown whether the classification results can be further improved by these different preprocess approaches when they are used at the same time. This study integrates different feature preprocessing steps for IAL, where feature extraction, feature selection and feature ordering are simultaneously employed. Experimental results indicate that such an integrated preprocessing approach is applicable for pattern classification performance improvement. Moreover, statistical significance testing also verified that such an integrated preprocessing approach is more suitable for the datasets with high-dimensional inputs.
KW - Feature Discrimination Ability
KW - Feature Extraction
KW - Feature Ordering
KW - Feature Selection
KW - Incremental Attribute Learning
KW - Neural Networks
KW - Pattern Classification
UR - http://www.scopus.com/inward/record.url?scp=84992035676&partnerID=8YFLogxK
M3 - Conference Proceeding
AN - SCOPUS:84992035676
T3 - FUSION 2016 - 19th International Conference on Information Fusion, Proceedings
SP - 386
EP - 393
BT - FUSION 2016 - 19th International Conference on Information Fusion, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Information Fusion, FUSION 2016
Y2 - 5 July 2016 through 8 July 2016
ER -