A K-Nearest Neighbor Based Approach for Determining the Weight Restrictions in Data Envelopment Analysis

Abstract

Data Envelopment Analysis (DEA), a method commonly used to measure the efficiency is becoming an increasingly popular management tool. On the contrary to classical efficiency approaches, the most important advantage of DEA is that researchers can determine the weight restrictions of input and output variables. Variable selection and determination of weight restrictions are important issues in DEA. This work investigates the use of K-nearest neighbor (KNN) algorithm in the definition of weight restrictions for DEA. With this purpose a new approach based on KNN is proposed. Applications are constructed with empirical and real data sets depending on the specific constraints. Performance scores were calculated for both KNN based restricted and unrestricted DEA models and the results are interpreted.

Keywords

• Data envelopment analysis
• Efficiency
• K-nearest neighbor
• Weight restrictions

Veri Zarflama Analizinde Ağırlık Kısıtlarının Belirlenmesinde K-En Yakın Komşuluğa Dayalı Bir Yaklaşım

Abstract

Genellikle etkinlik ölçümünde kullanılan Veri Zarflama Analizi (VZA), popüler bir yönetim aracı olmaya başlamıştır. Klasik etkinlik yaklaşımlarının tersine, VZA’nın en önemli avantajı, girdi ve çıktı değişkenlerinin ağırlık kısıtlarını araştırmacıların belirleyebilmesidir. Değişken seçimi ve ağırlık kısıtlarının belirlenmesi VZA’da önemli konulardır. Bu çalışma VZA için ağırlık kısıtlarının tanımlanmasında K-en yakın komşuluk algoritmasının kullanımını araştırmaktadır. Bu amaçla K-en yakın komşuluk temeline dayanan yeni bir yaklaşım önerilmiştir. Belirlenen kısıtlara bağlı olarak ampirik ve gerçek veri setleri ile uygulamalar yapılmıştır. K-en yakın komşu temelinde kısıtlı model ve ağılık kısıtlamasız VZA modeli için performans skorları hesaplanmıştır ve sonuçlar yorumlanmıştır.

Keywords

• Ağırlık kısıtları
• Etkinlik
• Veri zarflama analizi
• K-en yakın komşuluk

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