Öznitelik Seçiminde Dilsel Kuvvetli Sinir Bulanık Sınıflayıcı Kullanımı

Bu çalışmada, örüntü tanımanın basamaklarından biri olan öznitelik seçimi, bulanık kümelere uygulanan ve veriler üzerinden eğitilen dilsel kuvvetlerle yapılmaktadır. Bulanık kurallarda kullanılan “az”, “çok fazla” ve “çok az” gibi sıfatlar özniteliklerin sınıf için önemini ortaya koymaktadır. Buna göre ilk evrede her sınıf için en uygun olan ortak ve bireysel öznitelikler seçilmektedir. Seçilen öznitelikler ikinci evrede Dilsel Kuvvetli Sinir-Bulanık Sınıflayıcı (DKSBS) ile sınıflanarak başarımı ölçülmektedir. DKSBS ağ tabanlı bir sınıflayıcı olup, öznitelik-sınıf ilişkisini bulanık kurallarla çok iyi ortaya koyan bir yapıdır. Böylece ayrıştırmayı zorlaştıran gürültü, ölçüm hataları içeren ya da ilgisiz olan öznitelikler elenerek, sınıflamada ayırt edici özelliği en iyi olan öznitelikler değerlendirilmeye alınmaktadır.

Anahtar Kelimeler:

Öznitelik seçimi, Dilsel kuvvetler, Sinir-bulanık sınıflayıcı. .

Usıng Neuro-Fuzzy Classıfıer Wıth Lıngustıc Hedges For Feature Selectıon

In this study, one of the important steps of pattern recognition is feature selection that can be made by linguistic hedges. The linguistic hedges are used in fuzzy rules and trained with supervised learning methods. Some adjectives such as “very”, “little”, “more or less”, and “rather” are used in fuzzy rules to reveal the importance of feature. According to these situations, the suitable discriminative features for every class should be selected in the first step. The selected features are classified using neuro-fuzzy classifier with linguistic hedges in the second step. Then the classification success is evaluated. In this way, noisy or irrelevant features are eliminated and, discriminative features in the classification are taken to evaluation.

Keywords:

Feature selection, Linguistic hedges,

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