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Multidestination Indoor Navigation Using Path Planning and WiFi Fingerprint Localization
(2018-09-11)
Chitsobhuk, Orachat
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Warunsin, Kulwarun
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Udomthanapong, Sornchai
This paper presents an indoor navigation system based on multi-destination path planning and WiFi fingerprint localization. A user is allowed to specify multiple destinations and can detour the route at any time. Path planning will automatically update path using 2-opt and A∗ algorithms. The revised route will be analyzed according to user's current position supplied from the WiFi RSS fingerprint positioning. Naïve Bayes classification is adopted to learn from the RSS fingerprint priors stored in the database. Extensive experiments are conducted and performance comparison is analyzed and demonstrates significant performance improvement and higher noise tolerance with integration of the probabilistic priors. It can be seen that the proposed system enables user experience for indoor navigation service with support for automatic route updating and navigation refinement according to localization.
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Effects of distance between classes and training dataset size on imbalance datasets
(2009-04-13)
Huy, Thach Nguyen
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Foitong, Sombut
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Udomthanapong, Sornchai
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Pinngern, Ouen
This paper analyzes the effects of distance between classes and training datasets size to XCS classifier system on imbalanced datasets. Our purpose is to answer the question whether the loss of performance incurred by the classifier faced with class imbalance problems stems from the class imbalance per se or it can be explained in some other ways. The experiments from 250 artificial imbalanced datasets show that XCS can perform well in some imbalance domains if the training datasets size is large enough and the distance between classes is appropriate. Thus, it dose not seem fair to correlate imbalance datasets directly to the loss performance of XCS. Through this research, we also know what kinds of datasets are suitable for training XCS and dealing with class imbalances alone will not always help improve performance of classifiers. © 2009 American Institute of Physics.
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Using accuracy-based learning classifier systems for imbalance datasets
(2008-10-06)
Udomthanapong, Sornchai
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Tamee, Kreangsak
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Pinngern, Ouen
XCS is one of the most powerful learning classifier systems. It combines reinforcement learning and genetic algorithm to create a set of rules representing the extracted knowledge from dataset. The main advantage of this system is to provide rule-based models that represent human-readable patterns. However, not too much public have yet been studied in imbalance dataset. In this paper, we propose a novel technique to develop XCS deal with imbalance dataset. The proposed technique uses adaptive perception rate for each rule to provide balance learning between major and minor class. The experiment show that the propose technique can classify all level of imbalance classes on the well-know Boolean logic benchmark task - multiplexer problem. ©2008 IEEE.

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