PERFORMANCE COMPARISON OF EXTREME LEARNING MACHINE AND ITS VARIANTS IN IMAGE CLASSIFICATION

Abstract

Image classification is a critical technology in various fields such as medicine, agriculture, security, and more. Machine learning algorithms, particularly Extreme Learning Machines (ELM), have gained attention due to their fast learning capabilities and high accuracy rates. However, the performance of ELM can vary depending on the dataset and application scenario. This study aims to evaluate and compare the performance of the ELM model and its improved variants—namely, the incremental extreme learning machine (IELM), online sequential extreme learning machine (OSELM), regularized extreme learning machine (RELM), and constrained extreme learning machine (CELM)—in the context of image classification. These models were tested on four distinct datasets: MNIST, brain MRI images, rice images, and images of vehicle drivers. The models' performances were evaluated based on success rates, precision, recall, and F1 scores. The results indicate that the CELM model achieved the highest success rate, while the IELM model demonstrated the lowest performance. These findings provide valuable insights into how the unique structural features of each model influence classification success. Furthermore, the differences in performance across the datasets offer important clues for selecting the most effective model based on dataset characteristics. By understanding the strengths and limitations of each model, researchers and practitioners can make informed decisions when addressing image classification challenges.

Keywords

• Extreme Learning Machines
• Image Classification
• Machine Learning

GÖRÜNTÜ SINIFLANDIRMADA AŞIRI ÖĞRENME MAKİNESİ VE VARYANTLARININ PERFORMANS KARŞILAŞTIRMASI

Abstract

Görüntü sınıflandırma tıp, tarım, güvenlik ve daha fazlası gibi çeşitli alanlarda kritik bir teknolojidir. Makine öğrenmesi algoritmaları, özellikle de Aşırı Öğrenme Makineleri (ELM), hızlı öğrenme yetenekleri ve yüksek doğruluk oranları nedeniyle dikkat çekmiştir. Bununla birlikte, ELM'nin performansı veri kümesine ve uygulama senaryosuna bağlı olarak değişebilir. Bu çalışma, ELM modelinin ve geliştirilmiş varyantlarının (artımlı aşırı öğrenme makinesi (IELM), çevrimiçi sıralı aşırı öğrenme makinesi (OSELM), düzenli aşırı öğrenme makinesi (RELM) ve kısıtlı aşırı öğrenme makinesi (CELM)) performansını görüntü sınıflandırma bağlamında değerlendirmeyi ve karşılaştırmayı amaçlamaktadır. Bu modeller dört farklı veri kümesi üzerinde test edilmiştir: MNIST, beyin MRI görüntüleri, pirinç görüntüleri ve araç sürücülerinin görüntüleri. Modellerin performansları başarı oranları, kesinlik, geri çağırma ve F1 puanlarına göre değerlendirilmiştir. Sonuçlar, CELM modelinin en yüksek başarı oranını elde ettiğini, IELM modelinin ise en düşük performansı gösterdiğini ortaya koymuştur. Bu bulgular, her modelin kendine özgü yapısal özelliklerinin sınıflandırma başarısını nasıl etkilediğine dair değerli bilgiler sağlamaktadır. Ayrıca, veri kümeleri arasındaki performans farklılıkları, veri kümesi özelliklerine göre en etkili modelin seçilmesi için önemli ipuçları sunmaktadır. Araştırmacılar ve uygulayıcılar, her modelin güçlü yönlerini ve sınırlamalarını anlayarak, görüntü sınıflandırma zorluklarını ele alırken bilinçli kararlar verebilirler.

Keywords

• Aşırı Öğrenme Makineleri
• Görüntü Sınıflandırma
• Makine Öğrenmesi

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