Probabilistic Value Selection for Space Efficient Model

Probabilistic Value Selection for Space Efficient Model

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 data...

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Bibliographic Details
Main Authors: NJOO, Gunarto Sindoro, ZHENG, Baihua, HSU, Kuo-Wei, PENG, Wen-Chih
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2020
Subjects:
preprocessing
data mining
value selection
model size reduction
entropy
information theory
Databases and Information Systems
Theory and Algorithms
Online Access:https://ink.library.smu.edu.sg/sis_research/5264
https://ink.library.smu.edu.sg/context/sis_research/article/6267/viewcontent/6._Probabilistic_Value_Selection_for_Space_Efficient__IEEE_MDM2020_.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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 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.

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