Fast Data Reduction With Granulation-Based Instances Importance Labeling

Data reduction has become greatly significant prior to applying instance-based machine learning algorithms in the Big Data era. Data reduction is used to reduce the size of data sets while retaining representative data. Existing algorithms, however, suffer from heavy computational cost and in having...

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Main Authors: Xiaoyan Sun, Lian Liu, Cong Geng, Shaofeng Yang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8585005/
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author Xiaoyan Sun
Lian Liu
Cong Geng
Shaofeng Yang
author_facet Xiaoyan Sun
Lian Liu
Cong Geng
Shaofeng Yang
author_sort Xiaoyan Sun
collection DOAJ
description Data reduction has become greatly significant prior to applying instance-based machine learning algorithms in the Big Data era. Data reduction is used to reduce the size of data sets while retaining representative data. Existing algorithms, however, suffer from heavy computational cost and in having tradeoff in size reduction rate and learning accuracy. In this paper, we propose a fast data reduction approach by using granular computing to label important instances, i.e., instances with higher contributions to the learning task. The original data set is first granulated into K granules by applying K-means to a mapped lower-dimension space. Then, the importance of each instance in every granule is labeled based on its Hausdorff distance. Those instances whose importance values are lower than an experimentally tuned threshold are eliminated. The presented algorithm is applied to k NN classification tasks with eighteen different sizes of data sets from the UCI repository, and its outstanding performance in classification accuracy, size reduction rate, and runtime is illustrated by comparing with seven data reduction methods. The experimental results demonstrate that the proposed algorithm can greatly reduce the computational cost and achieve a higher classification accuracy when the reduction size is the same for all the compared algorithms.
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spelling doaj.art-b0067616156646b88162e4feb70204152022-12-21T23:36:05ZengIEEEIEEE Access2169-35362019-01-017335873359710.1109/ACCESS.2018.28891228585005Fast Data Reduction With Granulation-Based Instances Importance LabelingXiaoyan Sun0https://orcid.org/0000-0002-1386-6853Lian Liu1https://orcid.org/0000-0002-7833-2131Cong Geng2Shaofeng Yang3Information and Control Engineering College, China University of Mining and Technology, Xuzhou, ChinaInformation and Control Engineering College, China University of Mining and Technology, Xuzhou, ChinaInformation and Control Engineering College, China University of Mining and Technology, Xuzhou, ChinaAsset Management Co., Ltd., China University of Mining and Technology, Xuzhou, ChinaData reduction has become greatly significant prior to applying instance-based machine learning algorithms in the Big Data era. Data reduction is used to reduce the size of data sets while retaining representative data. Existing algorithms, however, suffer from heavy computational cost and in having tradeoff in size reduction rate and learning accuracy. In this paper, we propose a fast data reduction approach by using granular computing to label important instances, i.e., instances with higher contributions to the learning task. The original data set is first granulated into K granules by applying K-means to a mapped lower-dimension space. Then, the importance of each instance in every granule is labeled based on its Hausdorff distance. Those instances whose importance values are lower than an experimentally tuned threshold are eliminated. The presented algorithm is applied to k NN classification tasks with eighteen different sizes of data sets from the UCI repository, and its outstanding performance in classification accuracy, size reduction rate, and runtime is illustrated by comparing with seven data reduction methods. The experimental results demonstrate that the proposed algorithm can greatly reduce the computational cost and achieve a higher classification accuracy when the reduction size is the same for all the compared algorithms.https://ieeexplore.ieee.org/document/8585005/Data reductiongranular computingdata importance label<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>NN
spellingShingle Xiaoyan Sun
Lian Liu
Cong Geng
Shaofeng Yang
Fast Data Reduction With Granulation-Based Instances Importance Labeling
IEEE Access
Data reduction
granular computing
data importance label
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title Fast Data Reduction With Granulation-Based Instances Importance Labeling
title_full Fast Data Reduction With Granulation-Based Instances Importance Labeling
title_fullStr Fast Data Reduction With Granulation-Based Instances Importance Labeling
title_full_unstemmed Fast Data Reduction With Granulation-Based Instances Importance Labeling
title_short Fast Data Reduction With Granulation-Based Instances Importance Labeling
title_sort fast data reduction with granulation based instances importance labeling
topic Data reduction
granular computing
data importance label
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url https://ieeexplore.ieee.org/document/8585005/
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