Abstract
Feature Ordering is a unique preprocessing step in Incremental Attribute Learning (IAL), where features are gradually trained one after another. In previous studies, feature ordering derived based upon each individual feature’s contribution is time-consuming. This study attempts to develop an efficient feature ordering algorithm by some evolutionary approaches. The feature ordering algorithm presented in this paper is based on a criterion of maximum mean of feature discriminability. Experimental results derived by ITID, a neural IAL algorithm, show that such a feature ordering algorithm has a higher probability to obtain the lowest classification error rate with datasets from UCI Machine Learning Repository.
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Wang, T., Guan, SU., Ting, T.O., Man, K.L., Liu, F. (2012). Evolving Linear Discriminant in a Continuously Growing Dimensional Space for Incremental Attribute Learning. In: Park, J.J., Zomaya, A., Yeo, SS., Sahni, S. (eds) Network and Parallel Computing. NPC 2012. Lecture Notes in Computer Science, vol 7513. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35606-3_57
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DOI: https://doi.org/10.1007/978-3-642-35606-3_57
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