Indoor positioning system is a challenging problem
due to the variety of environment and unreliable of data that are
used for a prediction of the position. For Wi-Fi based indoor
positioning system, signal intensity used to predict the co-ordinate
of the device are known to fluctuate greatly despite being
measured at the same position. Therefore, significant errors are
often found when solving this problem with regression algorithms.
A quantization of co-ordinate data into position IDs can mitigate
the fluctuated noises in the data and is able to reformulate the
problem into a multi-class classification problem. The error in
positioning can then be computed from the distance between the
true co-ordinate and the predicted co-ordinate. The experiment
shows that Random forest classification can predict the position
with the error in positing at 5.65 meters on average when the
quantization is applied with threshold setting to 1 meter
