LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy



LibD3C

     LibD3C employs two types of selective ensemble techniques, which are a combination of the ensemble pruning based on k-means clustering and dynamic selection and circulating combination.We upgrade the software to LibD3C to handle imbalance data problem.The input file should be a .arff file. You can use this tool in any OS with JVM. software can only identify .arff file

Usage

downLoad usage.ppt

doc_usage.rar( Download the jar of libD3c and example )

Video

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CrossValidation

     -c means crossvalidation fold-num is the crossValiditon folds. trainFile is the file path for the training data


prediction

(1)train model

     -t means train model and the model would be saved in the same filePath(a new file "train.model" will be created) ,trainFile is the file path for the training data,resultFile is the file path save prediction result


(2)predict instance

     -p means prediction and train.model is the filePath you trained before ,trainFile is the file path for the training data, resultFile is the file path save prediction result

Download

    You should download LibD3C.jar and classifier.xml.You should also put them together(in the same file path).We have got our project on Github(https://github.com/guojiasheng/LibD3C-1.1)

All Rights Reserved Copyright @ 2015|Prof. Quan Zou
Last Modified in 2016/1/20