Hybrid Machine Learning Classifiers for Indoor User Localization Problem
Hamza Turabieh1, Ahmad Alghamdi2

1Hamza Turabieh*, Department of Information Technology, Taif University, Taif, Saudi Arabia.
2Ahmad S. Alghamdi, Computer Engineering Department, Taif University, Taif, Saudi Arabia.

Manuscript received on December 13, 2020. | Revised Manuscript received on January 05, 2020. | Manuscript published on January 10, 2021. | PP: 49-53 | Volume-10 Issue-3, January 2021 | Retrieval Number: 100.1/ijitee.C83750110321| DOI: 10.35940/ijitee.C8375.0110321
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Wi-Fi technology is now everywhere either inside or outside buildings. Using Wi-fi technology introduces an indoor localization service(s) (ILS). Determining indoor user location is a hard and complex problem. Several applications highlight the importance of indoor user localization such as disaster management, health care zones, Internet of Things applications (IoT), and public settlement planning. The measurements of Wi-Fi signal strength (i.e., Received Signal Strength Indicator (RSSI)) can be used to determine indoor user location. In this paper, we proposed a hybrid model between a wrapper feature selection algorithm and machine learning classifiers to determine indoor user location. We employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm as a feature selection to select the most active access point (AP) based on RSSI values. Six different machine learning classifiers were used in this work (i.e., Decision Tree (DT), Support Vector Machine (SVM), k-nearest neighbors (kNN), Linear Discriminant Analysis (LDA), Ensemble-Bagged Tree (EBaT), and Ensemble Boosted Tree (EBoT)). We examined all classifiers on a public dataset obtained from UCI repository. The obtained results show that EBoT outperforms all other classifiers based on accuracy value.
Keywords: Machine learning, Indoor user location, Classifications, Feature selection.