Tahmin performansını arttırmak için kolektif öğrenme yaklaşımı: Engelli bireylere yönelik deneysel çalışma

Year 2021, Volume: 10 Issue: 1, 100 - 114, 15.01.2021

Melda Kokoç , Fatih Kokoç

https://doi.org/10.28948/ngumuh.693303

Abstract

Fazla değişken söz konusu olduğunda elle sınıflama yapmak zaman ve emek isteyen bir süreç haline gelmektedir. Böyle bir duruma örnek olan engelli bireylerin öz bakım aktivitelerinde yaşadıkları sorunlara göre sınıflandırılması, uzman terapistler için zaman alıcı bir süreçtir. Bu çalışmanın amacı uzman terapistlere zaman kazandırması açısından fiziksel ve motor engelli bireylerin öz bakım aktivitelerinde yaşadıkları sorunları tahmin edebilecek bir modelin geliştirilmesidir. Tahmin sürecinde yedi farklı (destek vektör makineleri, yapay sinir ağları, C5.0, CART, QUEST, CHAID, bayes ağları) veri madenciliği algoritmasından yararlanılmıştır. Söz konusu algoritmalar, tek olarak ve farklı kolektif modeller oluşturularak uygulanmıştır. Tek ve kolektif olarak uygulanan modellerin deneysel sonuçları kıyaslandığında, iki veya daha fazla algoritmanın birleştirilmesi ile uygulanan kolektif öğrenme tekniğinin tahmin performansını yükselttiği görülmüştür.

Keywords

fiziksel ve motor engel, medikal veri, öz bakım, ICF-CY, veri madenciliği

Ensemble learning approach for enhancing performance prediction: experimental study for disabled people

Abstract

In the case of too many variables, manual classification becomes a time- and labor-intensive process. For instance, classifying people with disabilities according to the problems they experience in self-care activities is a time-consuming process for specialist therapists. The aim of this study is to develop a model that can predict the problems experienced by physically and motor disabled individuals in self-care activities in order to reduce time spent by specialist therapist. Seven different data mining algorithms (support vector machines, artificial neural networks, C5.0, CART, QUEST, CHAID, bayesian networks) have been used in the estimation process. These algorithms have been applied individually and by forming different ensemble models. When experimental results of single and ensemble models were compared, it was seen that ensemble learning technique combined with two or more algorithms increased predictive performance.

Keywords

data mining, physical and motor disability, medical data, self-care, ICF-CY

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