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Journals / Acta Infologica / 2022 / Volume: 6 Issue: 2

A Comparison of Decision Tree Algorithms for Indoor User Localization Using Wireless Signal Strength

Kablosuz Sinyal Gücünü Kullanarak İç Mekan Kullanıcı Lokalizasyonu için Karar Ağacı Algoritmalarının Karşılaştırılması

Ebru Efeoğlu(Kütahya Dumlupınar Üniversitesi, Yazılım Mühendisliği Bölümü)0000-0001-5444-6647
Pages
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Publication Date
2022-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

All metric values calculated before and after the cross-validation of the random forest algorithm were higher than those for the other algorithms.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Localizing users and devices indoors has a wide range of applications.
📊
Key Findings & Data:For this purpose, the study uses 12 different algorithms and compares their performances by conducting a performance analysis.
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Conclusion & Impact:All metric values calculated before and after the cross-validation of the random forest algorithm were higher than those for the other algorithms.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Indoor · Decision Trees · Wi-Fi

Abstract

Localizing users and devices indoors has a wide range of applications. Smart home systems can be used to locate criminals in restricted areas and determine the number of users at an access point. The aim of this study is to determine the location of users indoors using wireless signal strength as well as the best decision tree classification algorithm that can be used in monitoring devices that will be designed. For this purpose, the study uses 12 different algorithms and compares their performances by conducting a performance analysis. The study uses 10- fold cross validation as the performance analysis method. While evaluating the performance, the algorithms’ classification performance were compared before and after the cross-validation. Due to the study using a balanced dataset, the performance metrics used for classifying balanced datasets have bene preferred in the performance analysis. As a result of the analysis, the random forest algorithm was observed to have achieved the best performance. All metric values calculated before and after the cross-validation of the random forest algorithm were higher than those for the other algorithms.

Özet

İç mekanda kullanıcı ve cihazları yerelleştirmek geniş bir uygulama alanına sahiptir. Akıllı ev sistemleri, sınırlı bölgelerdeki suçluları bulma, bir erişim noktasındaki kullanıcı sayısını belirlemek için kullanılabilir. Bu çalışmanın amacı kablosuz sinyal gücüne dayalı olarak iç mekanda kullanıcıların konumunu belirlemektir. Bunun yanı sıra tasarlanacak izleme cihazlarında kullanılabilecek en iyi karar ağacı sınıflandırma algoritmasını saptamaktır. Bu amaçla çalışmada 12 farklı algoritma kullanılmış ve performans analizi yapılarak algoritmaların performansları karşılaştırılmıştır. Performans analiz yöntemi olarak 10 kat çapraz doğrulama kullanılmıştır. Performans değerlendirmesi yapılırken algoritmaların hem çaprazdoğrulama yapılmadan önceki sınıflandırma performansı hemde çapraz doğrulama sonrası yapılan sınıflandırma performansları karşılaştırılmışır. Çalışmada Dengeli bir veri seti kullanıldığı için Performans analizinde dengeli veri setlerinin sınıflandırılmasında kullanılan prformans metrikleri tercih edilmiştir. Performans analizinde doğruluk, karışıklık matrisi, kesinlik, duyarlılık, F-skoru, Kappa istatistiği, Kök ortalama hata değeri ve ROC değeri kullanılmıştır. Analiz sonucunda Analizden sonra. en iyi performansı Random Forest Rasgele orman algoritmasının elde ettiği gözlemlenmiştir. Algoritmanın çapraz doğrulama öncesi ve sonrasında hesaplanan tüm metric değerleri diğer algoritmalardan daha yüksektir.

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Cite This Article

Efeoğlu, E. (2022). A Comparison of Decision Tree Algorithms for Indoor User Localization Using Wireless Signal Strength. Acta Infologica, Volume: 6 Issue: 2. https://doi.org/10.26650/acin.1076352

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