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

Algorithm 1

CEnsLoc training and prediction algorithm.

	Input: training dataset with total A predictors
	Missing value replacement MVr
	Maximum number of clusters Kmax
	Output: predicted location Lx
	For training:
	Replace empty values with MVr
	Apply PCA on dataset to generate A’ predictors
	For k = 1 -> Kmax
	Generate clusters
	Generate and save k data subsets
	For each p є k data subsets
	Train p RFE using Algorithm 2 (training)
	Calculate performance measures
	End for
	End for
	Choose optimal configuration
	Save respective models for GMM, all RFEs
	For prediction at a new point x:
	Replace missing values with MVr
	Apply PCA on the FP
	Match one cluster Cmatch
	Invoke RFE of Cmatch using Algorithm 2 (prediction)