Performance Comparison between Two Interpretations of Missing Data using Matrix-Characterized Approximations

International Journal of Research and Engineering

View Publication Info
 
 
Field Value
 
Title Performance Comparison between Two Interpretations of Missing Data using Matrix-Characterized Approximations
 
Creator Min, Myat Myat
Soe, Thin Thin
 
Description Nowadays, the veracity related with data quality such as incomplete, inconsistent, vague or noisy data creates a major challenge to data mining and data analysis. Rough set theory presents a special tool for handling the incomplete and imprecise data in information systems. In this paper, rough set based matrix-represented approximations are presented to compute lower and upper approximations. The induced approximations are conducted as inputs for data analysis method, LERS (Learning from Examples based on Rough Set) used with LEM2 (Learning from Examples Module, Version2) rule induction algorithm. Analyzes are performed on missing datasets with “do not care” conditions and missing datasets with lost values. In addition, experiments on missing datasets with different missing percent by using different thresholds are also provided. The experimental results show that the system outperforms when missing data are characterized as “do not care” conditions than represented as lost values.
 
Publisher IJRE Publisher
 
Date 2019-03-15
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://digital.ijre.org/index.php/int_j_res_eng/article/view/375
10.21276/ijre.2019.6.2.3
 
Source International Journal of Research and Engineering; Vol 6 No 2 (2019): March 2019 Edition; 589-595
2348-7860
2348-7852
 
Language eng
 
Relation https://digital.ijre.org/index.php/int_j_res_eng/article/view/375/337
 
Rights Copyright (c) 2019 Myat Myat Min, Thin Thin Soe
http://creativecommons.org/licenses/by/4.0
 

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