Kanser Sınıflandırma için Logaritmik Sıra Birleştirme Yöntemini Kullanan Yeni Topluluk Öznitelik Seçim Tekniği

Year 2024, , 1000 - 1035, 29.04.2024

Hüseyin Güney , Hüseyin Öztoprak

https://doi.org/10.29130/dubited.1225446

Abstract

Son araştırmalar, topluluk öznitelik seçiminin (TÖS) mikrodizi veri sınıflandırmasında olağanüstü bir başarı elde ettiğini göstermiştir. Bununla birlikte, yetersiz birleştirme yöntemleri ve optimize edilmemiş ÖS teknikleri gibi konuların kısmen çözülmüş olarak kaldığı görülmektedir. Bu çalışma, TÖS yöntemlerinde özellik birleştirmeyi geliştirmek için logaritmik sıralama birleştirme (LRA) yöntemini önerdi. Ek olarak, önerilen yöntemin performansını geliştirmek için birkaç yöntemle birlikte kullanan hibrit yöntemler sunulmuştur. Ayrıca, öznitelik seçiminin optimizasyonunun etkisini ölçmek için optimize edilmiş öznitelik seçim tekniğinden elde edilen öznitelik sıralamalarına da önerilen yöntem uygulanmıştır. Hazırlanan deney, beş ikili mikrodizi veri seti üzerinde gerçekleştirilmiş olup, deney sonuçları, LRA'nın ortalama sıra birleştirme yöntemine (MRA) kıyaslanabilir bir sınıflandırma performansı sağladığını ve gen seçim istikrarı açısından MRA'dan daha iyi performans elde ettiğini göstermiştir. Ek olarak, hibrit teknikler, MRA ile aynı veya daha iyi sınıflandırma doğruluğu sağladı ve gen seçim istikrarını önemli ölçüde artırdı. Ayrıca önerilen bazı konfigürasyonlar, MRA'dan daha iyi doğruluk, hassasiyet ve özgüllük performansına ulaştı. Ayrıca, optimize edilmiş LRA, optimize edilmemiş LRA ve MRA'ya kıyasla gen seçim istikrarını önemli ölçüde iyileştirmiştir. Son olarak, sonuçlar diğer çalışmalarla karşılaştırıldığında, optimize edilmiş LRA'nın dikkate değer bir gen se.im istikrarı sağladığı görülmüştür ve bu çalışmanın bu alanda çalışan uzmanların kanser teşhisinde nispeten daha küçük bir gen kümesi kullanarak daha isabetli teşhis koymalarına yardımcı olabileceği vurgulanmıştır.

Keywords

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)

A Novel Ensemble Feature Selection Technique for Cancer Classification Using Logarithmic Rank Aggregation Method

Abstract

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.

Keywords

Microarray Data, Ensemble Learning, Aggregation Methods, Support Vector Machine Recursive Feature Elimination (SVM-RFE)

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