A priori verification and validation study of RFKON database

Ugur Yayan, Sinem Bozkurt, Ahmet Yazıcı, Serkan Gunal


In literature, there are studies that consider only one type of measurements such as Wi-Fi or Bluetooth RSS, but these values are not sufficient alone to overcome the problems in dynamically changed environments. In order to deal with this, we propose a novel fingerprint database that contains both Wi-Fi and Bluetooth RSS values in addition to magnetic field measurements obtained from mobile devices. On the other hand, this study presents a verification and validation of RFKON database to determine suitable machine learning algorithm and compare performance of these algorithms that by using feature selection algorithms. The aim of this study is to show performance of the classifiers in the RFKON database. For this purpose, different classifier algorithms which are deterministic algorithms such as k-nearest neighbor, Support Vector Machine, decision tree and probabilistic algorithms such as Naïve Bayes and Bayesian Networks are tested using this database. In addition to these tests, ensemble learning algorithms, namely AdaBoost and Bagging, are used to improve the performance of the selected classifier. Also, feature selection algorithms are applied to improve the performance of the selected classifiers.  As a conclusion, selected algorithms test results are reevaluated using multi-criteria optimization technique in order to find admissible algorithm in terms of both accuracy and computation time.

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