Genetik Algoritma Tabanlı Kısmi En Küçük Kareler Regresyonu için Değişken Seçimi

Kısmi En Küçük Kareler Regresyonu (KEKKR), bilimsel araştırmaların birçok alanında çoklu doğrusal bağlantı probleminin üstesinden gelmede sıradan en küçük karelere bir alternatif oluşturmaktadır. KEKKR yönteminin temelinde regresyon modeli ile iç içe geçmiş bir boyut indirgeme tekniği yer almaktadır. Bu çalışmada, genetik algoritma-kısmi en küçük kareler regresyonu (GAKEKK) incelenmiştir. Bu yöntemde, değişken seçiminde kullanılan KEKK ile güçlü optimizasyon yöntemleri olan GA birleştirilmiştir. İz düşüm için değişken önemi, KEKK ağırlıklarının ağırlıklandırılmış kareler toplamı olarak isimlendirilmekte ve hem X hem de Y’i modellemede bir değişkenin önemini özetlemektedir (Wold ve arkadaşları, 2001). Bu çalışmada, daha küçük hataya sahip modeli belirlemede GAKEKK tahmin modeli, KEKK-NIPALS modeli ve KEKK-VIP yöntemlerinin performans karşılaştırmaları R2adj değerleri kullanılarak incelenmiştir.

Anahtar Kelimeler:

Değişken seçimi, Genetik algoritma, İz düşüm için değişken önemi, Kısmi en küçük kareler regresyonu

Genetic Algorithm Based Variable Selection For Partial Least Squares Regression

Partial Least Squares (PLS) regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research. At the core of the PLS methodology lies a dimension reduction technique coupled with a regression model. In this paper, we investigate the genetic algorithms-partial least square regression (GAPLSR). This technique combines genetic algorithms as powerful optimization methods with PLS as a statistical method for variable selection. Variable importance for projection is a weighted sum of squares of the PLS-weights and thus a summary of the importance of a variable for the modeling of both X and Y (Wold et al., 2001). In this study, comparisons of R2adj values of GAPLSR predicting model, PLSR-NIPALS model and significant model PLSR-VIP were established according to the VIP scores of PLSR model to see which one has established a model with less error.

Keywords:

Genetic algorithms, Partial least square regressions, Variable selection, Variable importance for projection,

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