Evolving Linear Discriminant in a Continuously Growing Dimensional Space for Incremental Attribute Learning

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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Communication dans un congrès
James J. Park; Albert Zomaya; Sang-Soo Yeo; Sartaj Sahni. 9th International Conference on Network and Parallel Computing (NPC), Sep 2012, Gwangju, South Korea. Springer, Lecture Notes in Computer Science, LNCS-7513, pp.482-491, 2012, Network and Parallel Computing. 〈10.1007/978-3-642-35606-3_57〉
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Ting Wang, Sheng-Uei Guan, T. Ting, Ka Man, Fei Liu. Evolving Linear Discriminant in a Continuously Growing Dimensional Space for Incremental Attribute Learning. James J. Park; Albert Zomaya; Sang-Soo Yeo; Sartaj Sahni. 9th International Conference on Network and Parallel Computing (NPC), Sep 2012, Gwangju, South Korea. Springer, Lecture Notes in Computer Science, LNCS-7513, pp.482-491, 2012, Network and Parallel Computing. 〈10.1007/978-3-642-35606-3_57〉. 〈hal-01551374〉

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