Incremental Versus Non-incremental: Data and Algorithms Based on Ordering Relations

Based on multi-dominance discernibility matrices, a non-incremental algorithm RIDDM and an incremental algorithm INRIDDM are proposed by means of Dominance-based Rough Set Approach. For the incremental algorithm, when a new object arrives, after updating one row or one column in the matrix, we could...

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Main Authors: Xiuyi Jia, Lin Shang, Jiajun Chen, Xinyu Dai
Format: Article
Language:English
Published: Springer 2011-02-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Online Access:https://www.atlantis-press.com/article/2131.pdf
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author Xiuyi Jia
Lin Shang
Jiajun Chen
Xinyu Dai
author_facet Xiuyi Jia
Lin Shang
Jiajun Chen
Xinyu Dai
author_sort Xiuyi Jia
collection DOAJ
description Based on multi-dominance discernibility matrices, a non-incremental algorithm RIDDM and an incremental algorithm INRIDDM are proposed by means of Dominance-based Rough Set Approach. For the incremental algorithm, when a new object arrives, after updating one row or one column in the matrix, we could get the updated rule sets. Time complexity analysis and experimental results show that the incremental algorithm INRIDDM is superior to some other non-incremental algorithms when dealing with large data sets. This paper also explores the influence of data saturation and data concentration on rule induction algorithms. We come to conclude that the data saturation and data concentration are important for the performance analysis of one learning algorithm.
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spelling doaj.art-a8faa8063e9a4d0e8fa49fbb37ece35c2022-12-22T02:57:10ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832011-02-014110.2991/ijcis.2011.4.1.10Incremental Versus Non-incremental: Data and Algorithms Based on Ordering RelationsXiuyi JiaLin ShangJiajun ChenXinyu DaiBased on multi-dominance discernibility matrices, a non-incremental algorithm RIDDM and an incremental algorithm INRIDDM are proposed by means of Dominance-based Rough Set Approach. For the incremental algorithm, when a new object arrives, after updating one row or one column in the matrix, we could get the updated rule sets. Time complexity analysis and experimental results show that the incremental algorithm INRIDDM is superior to some other non-incremental algorithms when dealing with large data sets. This paper also explores the influence of data saturation and data concentration on rule induction algorithms. We come to conclude that the data saturation and data concentration are important for the performance analysis of one learning algorithm.https://www.atlantis-press.com/article/2131.pdfIncremental rule inductiondominance-based rough set approachdata saturationdata concentration.
spellingShingle Xiuyi Jia
Lin Shang
Jiajun Chen
Xinyu Dai
Incremental Versus Non-incremental: Data and Algorithms Based on Ordering Relations
International Journal of Computational Intelligence Systems
Incremental rule induction
dominance-based rough set approach
data saturation
data concentration.
title Incremental Versus Non-incremental: Data and Algorithms Based on Ordering Relations
title_full Incremental Versus Non-incremental: Data and Algorithms Based on Ordering Relations
title_fullStr Incremental Versus Non-incremental: Data and Algorithms Based on Ordering Relations
title_full_unstemmed Incremental Versus Non-incremental: Data and Algorithms Based on Ordering Relations
title_short Incremental Versus Non-incremental: Data and Algorithms Based on Ordering Relations
title_sort incremental versus non incremental data and algorithms based on ordering relations
topic Incremental rule induction
dominance-based rough set approach
data saturation
data concentration.
url https://www.atlantis-press.com/article/2131.pdf
work_keys_str_mv AT xiuyijia incrementalversusnonincrementaldataandalgorithmsbasedonorderingrelations
AT linshang incrementalversusnonincrementaldataandalgorithmsbasedonorderingrelations
AT jiajunchen incrementalversusnonincrementaldataandalgorithmsbasedonorderingrelations
AT xinyudai incrementalversusnonincrementaldataandalgorithmsbasedonorderingrelations