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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.
https://hal.inria.fr/hal-01551374
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, June 30, 2017 - 10:36:20 AM
Last modification on : Thursday, March 5, 2020 - 5:40:42 PM
Long-term archiving on: : Monday, January 22, 2018 - 9:25:32 PM
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, June 30, 2017 - 10:36:20 AM
Last modification on : Thursday, March 5, 2020 - 5:40:42 PM
Long-term archiving on: : Monday, January 22, 2018 - 9:25:32 PM
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Distributed under a Creative Commons Attribution 4.0 International License