Local radial basis function network regressor with feature importance optimization

Yu Ann Chen, Pau-Choo Chung

研究成果: Conference contribution

摘要

Recent big data analysis usually involves datasets with features collected from various sources, where each feature may have different importance, and the training datasets may not be uniformly sampled. To improve the prediction quality of realworld learning problems, we propose a local radial basis function network that is capable of handling both nonuniform sampling density and heterogeneous features. Nonuniform sampling is resolved by estimating local sampling density and adjust the width of the Gaussian kernels accordingly, and heterogeneous features are handled by scaling each dimension of the feature space asymmetrically. To make the learner aware of inter-feature relationship, we propose a feature importance optimization technique base on L-BFGS-B algorithm, using the leave-one-out cross-validation mean squared error as the objective function. Leave-one-out cross-validation used to be a very time consuming process, but the optimization has been made practical by the fast cross-validation capability of local RBFN. Our experiments show that when both nonuniform sampling density and interfeature relationship are properly handled, a simple RBFN can outperform more complex kernel-based learning models such as support vector regressor on both mean-squared-error and training speed.

原文English
主出版物標題2015 International Joint Conference on Neural Networks, IJCNN 2015
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781479919604, 9781479919604, 9781479919604, 9781479919604
DOIs
出版狀態Published - 2015 9月 28
事件International Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
持續時間: 2015 7月 122015 7月 17

出版系列

名字Proceedings of the International Joint Conference on Neural Networks
2015-September

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2015
國家/地區Ireland
城市Killarney
期間15-07-1215-07-17

All Science Journal Classification (ASJC) codes

  • 軟體
  • 人工智慧

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