A Novel Ensemble Feature Selection Technique for Cancer Classification Using Logarithmic Rank Aggregation Method
Recent studies have shown that ensemble feature selection (EFS) has achieved outstanding performance in microarray data classification. However, some issues remain partially resolved, such as suboptimal aggregation methods and non-optimised underlying FS techniques. This study proposed the logarithm...
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Düzce University
2024-04-01
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| Series: | Düzce Üniversitesi Bilim ve Teknoloji Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/2857576 |
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| author | Hüseyin Öztoprak Hüseyin Güney |
| author_facet | Hüseyin Öztoprak Hüseyin Güney |
| author_sort | Hüseyin Öztoprak |
| collection | DOAJ |
| description | Recent studies have shown that ensemble feature selection (EFS) has achieved outstanding performance in microarray data classification. However, some issues remain partially resolved, such as suboptimal aggregation methods and non-optimised underlying FS techniques. This study proposed the logarithmic rank aggregate (LRA) method to improve feature aggregation in EFS. Additionally, a hybrid aggregation framework was presented to improve the performance of the proposed method by combining it with several methods. Furthermore, the proposed method was applied to the feature rank lists obtained from the optimised FS technique to investigate the impact of FS technique optimisation. The experimental setup was performed on five binary microarray datasets. The experimental results showed that LRA provides a comparable classification performance to mean rank aggregation (MRA) and outperforms MRA in terms of gene selection stability. In addition, hybrid techniques provided the same or better classification accuracy as MRA and significantly improved stability. Moreover, some proposed configurations had better accuracy, sensitivity, and specificity performance than MRA. Furthermore, the optimised LRA drastically improved the FS stability compared to the unoptimised LRA and MRA. Finally, When the results were compared with other studies, it was shown that optimised LRA provided a remarkable stability performance, which can help domain experts diagnose cancer diseases with a relatively smaller subset of genes. |
| format | Article |
| id | doaj-art-593cd99bb2c24be8876ca347005f046c |
| institution | Kabale University |
| issn | 2148-2446 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Düzce University |
| record_format | Article |
| series | Düzce Üniversitesi Bilim ve Teknoloji Dergisi |
| spelling | doaj-art-593cd99bb2c24be8876ca347005f046c2025-08-20T03:53:52ZengDüzce UniversityDüzce Üniversitesi Bilim ve Teknoloji Dergisi2148-24462024-04-011221000103510.29130/dubited.122544697A Novel Ensemble Feature Selection Technique for Cancer Classification Using Logarithmic Rank Aggregation MethodHüseyin Öztoprak0https://orcid.org/0000-0003-1853-3510Hüseyin Güney1https://orcid.org/0000-0001-7924-1904CYPRUS INTERNATIONAL UNIVERSITYBAHÇEŞEHİR KIBRIS ÜNİVERSİTESİRecent studies have shown that ensemble feature selection (EFS) has achieved outstanding performance in microarray data classification. However, some issues remain partially resolved, such as suboptimal aggregation methods and non-optimised underlying FS techniques. This study proposed the logarithmic rank aggregate (LRA) method to improve feature aggregation in EFS. Additionally, a hybrid aggregation framework was presented to improve the performance of the proposed method by combining it with several methods. Furthermore, the proposed method was applied to the feature rank lists obtained from the optimised FS technique to investigate the impact of FS technique optimisation. The experimental setup was performed on five binary microarray datasets. The experimental results showed that LRA provides a comparable classification performance to mean rank aggregation (MRA) and outperforms MRA in terms of gene selection stability. In addition, hybrid techniques provided the same or better classification accuracy as MRA and significantly improved stability. Moreover, some proposed configurations had better accuracy, sensitivity, and specificity performance than MRA. Furthermore, the optimised LRA drastically improved the FS stability compared to the unoptimised LRA and MRA. Finally, When the results were compared with other studies, it was shown that optimised LRA provided a remarkable stability performance, which can help domain experts diagnose cancer diseases with a relatively smaller subset of genes.https://dergipark.org.tr/tr/download/article-file/2857576mikrodizi veri kümesitopluluk öğrenmebirleştirme yöntemleridestek vektör makineleritekrarlayan öznitelik seçimi (svm-rfe)microarray dataensemble learningaggregation methodssupport vector machine recursive feature elimination (svm-rfe)microarray dataensemble learningaggregation methodssupport vector machine recursive feature elimination (svm-rfe) |
| spellingShingle | Hüseyin Öztoprak Hüseyin Güney A Novel Ensemble Feature Selection Technique for Cancer Classification Using Logarithmic Rank Aggregation Method Düzce Üniversitesi Bilim ve Teknoloji Dergisi mikrodizi veri kümesi topluluk öğrenme birleştirme yöntemleri destek vektör makineleri tekrarlayan öznitelik seçimi (svm-rfe) microarray data ensemble learning aggregation methods support vector machine recursive feature elimination (svm-rfe) microarray data ensemble learning aggregation methods support vector machine recursive feature elimination (svm-rfe) |
| title | A Novel Ensemble Feature Selection Technique for Cancer Classification Using Logarithmic Rank Aggregation Method |
| title_full | A Novel Ensemble Feature Selection Technique for Cancer Classification Using Logarithmic Rank Aggregation Method |
| title_fullStr | A Novel Ensemble Feature Selection Technique for Cancer Classification Using Logarithmic Rank Aggregation Method |
| title_full_unstemmed | A Novel Ensemble Feature Selection Technique for Cancer Classification Using Logarithmic Rank Aggregation Method |
| title_short | A Novel Ensemble Feature Selection Technique for Cancer Classification Using Logarithmic Rank Aggregation Method |
| title_sort | novel ensemble feature selection technique for cancer classification using logarithmic rank aggregation method |
| topic | mikrodizi veri kümesi topluluk öğrenme birleştirme yöntemleri destek vektör makineleri tekrarlayan öznitelik seçimi (svm-rfe) microarray data ensemble learning aggregation methods support vector machine recursive feature elimination (svm-rfe) microarray data ensemble learning aggregation methods support vector machine recursive feature elimination (svm-rfe) |
| url | https://dergipark.org.tr/tr/download/article-file/2857576 |
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