Mobile Information Systems / 2018 / Article / Alg 2

Research Article

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 nodesStep 2: Produce the resulting ensemble of trees {Tl}1NtreeFor location prediction at a new point x from RFE LrfNtree:Let, Lm(x) be the room/class prediction by the mth RFE treeLrfNtree(x) = maj. vote {Lm(x)}1Ntree

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