The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection
Purpose: The purpose of this study is to enhance data quality and overall accuracy and improve certainty by reducing the negative impacts of the FCM algorithm while clustering real-world data and also decreasing the inherent noise in data sets. Design/methodology/approach: The present study proposed...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
Emerald Group Holdings Ltd.
2020
|
| Subjects: |
| _version_ | 1796865724085436416 |
|---|---|
| author | Nematzadeh, Zahra Ibrahim, Roliana Selamat, Ali Nazerian, Vahdat |
| author_facet | Nematzadeh, Zahra Ibrahim, Roliana Selamat, Ali Nazerian, Vahdat |
| author_sort | Nematzadeh, Zahra |
| collection | ePrints |
| description | Purpose: The purpose of this study is to enhance data quality and overall accuracy and improve certainty by reducing the negative impacts of the FCM algorithm while clustering real-world data and also decreasing the inherent noise in data sets. Design/methodology/approach: The present study proposed a new effective model based on fuzzy C-means (FCM), ensemble filtering (ENS) and machine learning algorithms, called an FCM-ENS model. This model is mainly composed of three parts: noise detection, noise filtering and noise classification. Findings: The performance of the proposed model was tested by conducting experiments on six data sets from the UCI repository. As shown by the obtained results, the proposed noise detection model very effectively detected the class noise and enhanced performance in case the identified class noisy instances were removed. Originality/value: To the best of the authors’ knowledge, no effort has been made to improve the FCM algorithm in relation to class noise detection issues. Thus, the novelty of existing research is combining the FCM algorithm as a noise detection technique with ENS to reduce the negative effect of inherent noise and increase data quality and accuracy. |
| first_indexed | 2024-03-05T21:01:25Z |
| format | Article |
| id | utm.eprints-93944 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:01:25Z |
| publishDate | 2020 |
| publisher | Emerald Group Holdings Ltd. |
| record_format | dspace |
| spelling | utm.eprints-939442022-02-28T13:18:40Z http://eprints.utm.my/93944/ The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection Nematzadeh, Zahra Ibrahim, Roliana Selamat, Ali Nazerian, Vahdat T55-55.3 Industrial Safety. Industrial Accident Prevention Purpose: The purpose of this study is to enhance data quality and overall accuracy and improve certainty by reducing the negative impacts of the FCM algorithm while clustering real-world data and also decreasing the inherent noise in data sets. Design/methodology/approach: The present study proposed a new effective model based on fuzzy C-means (FCM), ensemble filtering (ENS) and machine learning algorithms, called an FCM-ENS model. This model is mainly composed of three parts: noise detection, noise filtering and noise classification. Findings: The performance of the proposed model was tested by conducting experiments on six data sets from the UCI repository. As shown by the obtained results, the proposed noise detection model very effectively detected the class noise and enhanced performance in case the identified class noisy instances were removed. Originality/value: To the best of the authors’ knowledge, no effort has been made to improve the FCM algorithm in relation to class noise detection issues. Thus, the novelty of existing research is combining the FCM algorithm as a noise detection technique with ENS to reduce the negative effect of inherent noise and increase data quality and accuracy. Emerald Group Holdings Ltd. 2020 Article PeerReviewed Nematzadeh, Zahra and Ibrahim, Roliana and Selamat, Ali and Nazerian, Vahdat (2020) The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection. Engineering Computations (Swansea, Wales), 37 (7). pp. 2337-2355. ISSN 0264-4401 http://dx.doi.org/10.1108/EC-05-2019-0242 |
| spellingShingle | T55-55.3 Industrial Safety. Industrial Accident Prevention Nematzadeh, Zahra Ibrahim, Roliana Selamat, Ali Nazerian, Vahdat The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection |
| title | The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection |
| title_full | The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection |
| title_fullStr | The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection |
| title_full_unstemmed | The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection |
| title_short | The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection |
| title_sort | synergistic combination of fuzzy c means and ensemble filtering for class noise detection |
| topic | T55-55.3 Industrial Safety. Industrial Accident Prevention |
| work_keys_str_mv | AT nematzadehzahra thesynergisticcombinationoffuzzycmeansandensemblefilteringforclassnoisedetection AT ibrahimroliana thesynergisticcombinationoffuzzycmeansandensemblefilteringforclassnoisedetection AT selamatali thesynergisticcombinationoffuzzycmeansandensemblefilteringforclassnoisedetection AT nazerianvahdat thesynergisticcombinationoffuzzycmeansandensemblefilteringforclassnoisedetection AT nematzadehzahra synergisticcombinationoffuzzycmeansandensemblefilteringforclassnoisedetection AT ibrahimroliana synergisticcombinationoffuzzycmeansandensemblefilteringforclassnoisedetection AT selamatali synergisticcombinationoffuzzycmeansandensemblefilteringforclassnoisedetection AT nazerianvahdat synergisticcombinationoffuzzycmeansandensemblefilteringforclassnoisedetection |