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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

User Localization in an Indoor Environment by Combining Different Algorithms through Plurality Rule

İç Mekanda Kullanıcı Lokalizasyonu için Farklı Makine Öğrenmesi Algoritmalarının Çoğulluk Kuralı ile Birleştirilmesi

How to cite: Ateş MC, Gümüşoğlu OE, Çolak A, Fescioğlu Ünver N. User localization in an indoor environment by combining different algorithms through plurality rule. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2020; 3(2): 64-68. DOI: 10.54856/jiswa.202012118

Full Text: PDF, in English.

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Title: User Localization in an Indoor Environment by Combining Different Algorithms through Plurality Rule

Abstract: User localization in an indoor environment has a wide application area including production and service systems such as factories, smart homes, hospitals, nursing homes, etc. User localization based on Wi-Fi signals has been widely studied using various classification algorithms. In this type of problem, several Wi-Fi routers placed in an indoor environment provide signals with different strengths depending on the location/room of the user. Most classification algorithms successfully make the localization with a high accuracy rate. However, in the current literature, there is no widely accepted 'best' algorithm for solving this problem. This study proposes the use of the plurality rule to combine several classification algorithms and obtain a single result. Plurality voting rule is an electoral system where the candidate that polls the most vote wins the election. We apply the plurality rule to the indoor localization problem and generate the Majority algorithm. The Majority algorithm takes the 'votes' of five different classification algorithms and provides a single result through plurality rule. Results show that the mean accuracy rate of the Majority algorithm is higher than the classification algorithms it combines. In addition, we show that proving a single accuracy rate is not sufficient for declaring that an algorithm is better than the other. Classification algorithms select the training and test data randomly and different divisions result in different accuracy rates. In this study, we show that comparing the classification algorithms through confidence intervals provides more accurate information.

Keywords: Indoor localization; wi-si signal strength; classification algorithms; plurality rule


Başlık: İç Mekanda Kullanıcı Lokalizasyonu için Farklı Makine Öğrenmesi Algoritmalarının Çoğulluk Kuralı ile Birleştirilmesi

Özet: İç mekan ortamında kullanıcı lokalizasyonu fabrikalar, akıllı evler, hastaneler, huzurevleri gibi üretim ve hizmet sistemlerini içeren geniş bir uygulama alanına sahiptir. Wi-Fi sinyallerine dayalı kullanıcı lokalizasyonu, çeşitli sınıflandırma algoritmaları kullanılarak geniş çapta incelenmiştir. Bu tür bir problemde, bir iç mekâna yerleştirilen birkaç Wi-Fi yönlendiricisi, kullanıcının konumuna bağlı olarak farklı güçlere sahip sinyaller sağlar. Çoğu sınıflandırma algoritması, kullanıcının konumunu yüksek doğruluk oranıyla tespit etmektedir. Bununla birlikte, mevcut literatürde bu sorunu çözmek için yaygın olarak kabul edilen bir 'en iyi' algoritma yoktur. Bu çalışma, çeşitli sınıflandırma algoritmalarını birleştirmek ve tek bir sonuç elde etmek için çoğulluk kuralının kullanımını önermektedir. Çoğul oylama kuralı, en çok oy alan adayın seçimi kazandığı bir seçim sistemidir. Bu çalışmada çoğulluk kuralı iç mekânda lokalizasyon problemine uygulanmış ve 'Çoğunluk Algoritması' geliştirilmiştir. Çoğunluk algoritması, beş farklı sınıflandırma algoritmasının 'oylarını' alır ve çoğulluk kuralı aracılığıyla tek bir sonuç sağlar. Sonuçlar, Çoğunluk algoritmasının ortalama doğruluk oranının, oylarını kullandığı bireysel sınıflandırma algoritmalarından daha yüksek olduğunu göstermektedir. Ayrıca çalışmada, bu problem için bir sınıflandırma algoritmasının diğerinden daha iyi olduğunu beyan etmek için tek bir doğruluk oranının kullanılmasının yeterli olmadığı gösterilmiştir. Sınıflandırma algoritmaları eğitim ve test verilerini rastgele ayırmakta ve farklı veri ayrımları farklı doğruluk oranlarına sebep olmaktadır. Bu çalışmada, sınıflandırma algoritmaları karşılaştırılırken güven aralıkları kullanılmasının daha doğru bir bilgi sağladığı gösterilmektedir.

Anahtar kelimeler: İç mekan lokalizasyonu; wi-fi sinyal gücü; sınıflandırma algoritmaları; çoğulluk kuralı


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