Fuzzy C means imputation of missing values with ant colony optimization
Missing value is an error that always happened, and it is unavoidable. This error should be handled correctly before data is processed into processing model. This paper proposes a improved method of imputation by employing a new version of Fuzzy c Means (FCM) which hybridized with Evolutionary Algor...
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| Main Authors: | , , |
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| Format: | Article |
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World Academy of Research in Science and Engineering
2020
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087832451&doi=10.30534%2fijatcse%2f2020%2f2191.32020&partnerID=40&md5=6c3bf89c5091f4dfddbcb012d094c87c http://eprints.utp.edu.my/23092/ |
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| Summary: | Missing value is an error that always happened, and it is unavoidable. This error should be handled correctly before data is processed into processing model. This paper proposes a improved method of imputation by employing a new version of Fuzzy c Means (FCM) which hybridized with Evolutionary Algorithm to handle missing values problem. Missing values can be treated by imputing the values. The advantage of FCM is it can provide a better separation of instances where it is not well separated. It is a well-known classification method that can provide highest accuracy. It can be benefit from Ant Colony Optimization that can help to select only highly related feature to be process as an estimation for a missing value. Here, a traditional FCM basic is test as a cluster technique for imputed data. © 2020, World Academy of Research in Science and Engineering. All rights reserved. |
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