Sınıflar Arası Kenar Payını Genişletmek İçin Yeni Bir Örnek Seçim Algoritması

Yıl 2022, Cilt: 5 Sayı: 2, 119 - 126, 21.09.2022

Fatih Aydın

https://doi.org/10.38016/jista.1033354

Cited By: 1

Öz

Veri kümelerindeki gereksiz örneklerin atılması öğrenme sürecini kısalttığı gibi gürültülü verileri ortadan kaldırdığı için öğrenme performansını da arttırmaktadır. Örnek seçim yöntemleri, yukarıda belirtilen görevleri yerine getirmek için yaygın olarak kullanılmaktadır. Bu makalede, "Border Instances Reduction using Classes Handily (BIRCH)" adlı yeni bir denetimli örnek seçim algoritması öneriyoruz. BIRCH, her örneğin k-en yakın komşularını dikkate alarak, sadece aynı sınıftan komşuları olan, yani farklı sınıflardan komşuları olmayan örnekleri seçer. BIRCH, çeşitli alanlardan on beş veri kümesi kullanılarak biri geleneksel ve dördü son teknoloji örnek seçim algoritması ile karşılaştırılmıştır. Ampirik sonuçlar, BIRCH'in komşu sayısının ayarlanmasıyla doğruluk oranı ve azaltma oranı arasındaki dengeyi iyi sağladığını göstermektedir. Ayrıca önerilen yöntem, yüksek bir sınıflandırma doğruluğunu sağlamayı garanti eder. Önerilen algoritmanın kaynak kodu https://github.com/fatihaydin1/BIRCH web adresinde bulunabilir.

Anahtar Kelimeler

Makine öğrenmesi, en yakın komşular, örnek azaltma, örnek seçimi, büyük veri

A New Instance Selection Method for Enlarging Margins Between Classes

Öz

As discarding superfluous instances in data sets shortens the learning process, it also increases learning performance because of eliminating noisy data. Instance selection methods are commonly utilized to undertake the abovementioned tasks. In this paper, we propose a new supervised instance selection algorithm called Border Instances Reduction using Classes Handily (BIRCH). BIRCH considers k-nearest neighbors of each instance and selects instances that have neighbors from the only same class, namely, but not having neighbors from the different classes. It has been compared with one traditional and four state-of-the-art instance selection algorithms by using fifteen data sets from various domains. The empirical results show BIRCH well delivers the trade-off between accuracy rate and reduction rate by tuning the number of neighbors. Furthermore, the proposed method guarantees to yield a high classification accuracy. The source code of the proposed algorithm can be found in https://github.com/fatihaydin1/BIRCH.

Anahtar Kelimeler

Machine learning, nearest neighbors, instance reduction, instance selection, big data

Kaynakça

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