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Adaboost extensions for cost-sentive classification
Adaboost extensions for cost-sentive classification
CSExtension 1
CSExtension 2
CSExtension 3
CSExtension 4
CSExtension 5
AdaCost
Boost
CostBoost
Uboost
CostUBoost
AdaBoostM1
Implementation of all the listed algorithms of the cluster "cost-sensitive classification".
They are the meta algorithms which requires base algorithms e.g. Decision Tree
Moreover,
Voting criteria is also required e.g. Minimum expected cost criteria
Input also requires to load an arff file and a cost matrix (sample arff and cost files are uploaded for users' reference)
This extension uses weka for classification and generates the classification model along with confusion matrix. For given dataset and cost matrix
Open-Sourced Computer-aided Voting Machine, intended to be small and simple, uses a touch-screen input with barcode and alpha-numeric output to a printed paper ballot.