CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles

Algorithm 2

RFE classification algorithm for training and prediction.

	Input: data subset with total A’ predictors for training
	No. of tree Ntree
	Allowed maximum no. of splits Smax
	Random no. of predictors/features f
	Output: estimated location Lx
	For system training:
	Step 1: for l = 1 to Ntree
(i)	Choose a bootstrap sample set (SS) of size (Nsample) with replacement from the training data subset
(ii)	Generate a Random Forest Tree (Tl) to SS, via recursively iterating (a-c) for every terminal node of tree, unless the maximum no. of splits (Smax) is reached(a)Randomly select f features/variables from the A’ predictors (f << A’)(b)Choose the best features/split-point from the f employing Gini Index(c)Split node forming into two children nodes Step 2: Produce the resulting ensemble of trees {Tl}1^Ntree For location prediction at a new point x from RFE Lrf^Ntree: Let, Lm(x) be the room/class prediction by the m^th RFE tree Lrf^Ntree(x) = maj. vote {Lm(x)}1^Ntree