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Enhancing indoor positioning based on partitioning cascade machine learning models



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This paper proposes the method, called Partitioning machine learning Classifier (PMLC), to enhance accuracy of fingerprinting indoor positioning by using machine Learning algorithms. PMLC exploits Limited information of the signal strength and combines a clustering task and a classification task. PMLC is compared with well-known machine Learning classifiers, i.e. Decision Tree, Naive Bayes, and Artificial Neural networks. The Performance comparison is done in terms of accuracy of position classification and precision of distance classifier. The result of this study shows that PMLC can increase performance for indoor positioning of all classifiers when an appropriate number of clusters is assigned in the clustering process. In addition, PMLC is the most optimized model while having Decision Tree to be its classifier. © 2014 IEEE.

Performance comparison (65 items found) | Learning classifiers (5 items found) | Learning algorithms (325 items found) | Limited information (8 items found) | indoor positioning (13 items found) | machine learning (171 items found) | Neural networks (1048 items found) | position (172 items found) | wireless deviceClustering algorithms | Machine learning models | Classification tasks | Distance classifiers | Learning systems | Wireless devices | Decision trees | Tracking |

ต้นฉบับข้อมูล : scopus