Kohonen Öğrenme Kuralı Yardımıyla Centroid Değerleri Güncelleme

Abstract

Metin madenciliği alanında öne çıkan görevlerden birisi metin sınıflandırmadır. Konuyla ilgili çok sayıda tümevarımsal öğrenim algoritması bulunmakta olup onlardan birisi de centroid tabanlı sınıflayıcıdır. Performansı naive bayes sınıflayıcı kadar iyi olan centroid sınıflayıcıların en büyük problemi model misfittir ve bu problemin çözümü için centroid değerlerinin güncellenmesine ihtiyaç vardır. Centroid güncelleme için bugüne kadar birçok yöntem geliştirilmiş olup bu çalışmada yeni bir yöntem önerilmektedir. Önerimiz; kohonen öğrenme kuralı yardımıyla centroid değerlerinin dinamik olarak güncellenmesi üzerinedir. Bu öneriye uygun olarak veriler üzerinde deneyler yapılmış ve kohonen öğrenme kuralının centroid sınıflayıcı performansını artırdığı gözlemlenmiştir.

Keywords

• centroid sınıflayıcı
• model misfit
• centroid güncelleme
• kohonen öğrenme

Updating Centroid Values Using Kohonen Learning Rule

Abstract

Text classification is one of the most prominent tasks in text mining. Numerous inductive learning algorithms exist, one of which is the centroid-based classifier. Centroid classifiers, which perform as well as naive Bayes classifiers, suffer from model misfit, a major problem requiring centroid values ​​to be updated. Many methods have been developed for centroid updating, and this study proposes a new method. Our proposal involves dynamically updating centroid values ​​using the Kohonen learning rule. Experiments have been conducted on data based on this proposal, and it has been observed that the Kohonen learning rule improves centroid classifier performance.

Keywords

• centroid classifier
• model misfit
• centroid updating
• kohonen learning

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