Abstract
Feature Ordering is a special training preprocessing for Incremental Attribute Learning (IAL), where features are trained one after another. Since most feature ordering calculation methods, compute feature ordering in one batch, no matter, this study presents a novel approach combining input feature ordered training and output partitioning for IAL to compute feature ordering with considering whether the output of the classification problem is univariate or multivariate. New metric called feature’s Single Sensibility (SS) is proposed to individually calculate features’ discrimination ability for each output. Finally, experimental benchmark results based on neural networks in IAL show that SS is applicable to calculates feature’s discrimination ability. Furthermore, combined output partitioning can also improve further the final classification performance effectively.
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Acknowledgments
This research is supported by National Nature Science Foundation of China under Grant No. 61332007, China Jiangsu Provincial Science and Technology Foundation under Grant No. BK20131182, and China Postdoctoral Science Foundation under Grant No. 2015M571042.
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Wang, T., Guan, SU., Ma, J., Liu, F. (2015). Linear Feature Sensibility for Output Partitioning in Ordered Neural Incremental Attribute Learning. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_37
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DOI: https://doi.org/10.1007/978-3-319-23862-3_37
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