Probabilistic Value Selection for Space Efficient Model

Gunarto Sindoro Njoo, Baihua Zheng, Kuo Wei Hsu, Wen-Chih Peng

研究成果: Conference contribution同行評審

摘要

An alternative to current mainstream preprocessing methods is proposed: Value Selection (VS). Unlike the existing methods such as feature selection that removes features and instance selection that eliminates instances, value selection eliminates the values (with respect to each feature) in the dataset with two purposes: reducing the model size and preserving its accuracy. Two probabilistic methods based on information theory's metric are proposed: PVS and \mathrm {P}^{+}VS. Extensive experiments on the benchmark datasets with various sizes are elaborated. Those results are compared with the existing preprocessing methods such as feature selection, feature transformation, and instance selection methods. Experiment results show that value selection can achieve the balance between accuracy and model size reduction.

原文English
主出版物標題Proceedings - 2020 21st IEEE International Conference on Mobile Data Management, MDM 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面148-157
頁數10
ISBN(電子)9781728146638
DOIs
出版狀態Published - 六月 2020
事件21st IEEE International Conference on Mobile Data Management, MDM 2020 - Versailles, France
持續時間: 30 六月 20203 七月 2020

出版系列

名字Proceedings - IEEE International Conference on Mobile Data Management
2020-June
ISSN(列印)1551-6245

Conference

Conference21st IEEE International Conference on Mobile Data Management, MDM 2020
國家France
城市Versailles
期間30/06/203/07/20

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