Text categorization using an ensemble classifier based on a mean co-association matrix
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Utilizing cumulative logit models and human computation on automated speech assessment
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
On the determination of inlining vectors for program optimization
CC'13 Proceedings of the 22nd international conference on Compiler Construction
Machine learning-based classifiers ensemble for credit risk assessment
International Journal of Electronic Finance
CBC: An associative classifier with a small number of rules
Decision Support Systems
Intelligent Data Analysis
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Two of the prime open-source environments available for machine/statistical learning in data mining and knowledge discovery are the software packages Weka and R which have emerged from the machine learning and statistics communities, respectively. To make the different sets of tools from both environments available in a single unified system, an R package RWeka is suggested which interfaces Weka’s functionality to R. With only a thin layer of (mostly R) code, a set of general interface generators is provided which can set up interface functions with the usual “R look and feel”, re-using Weka’s standardized interface of learner classes (including classifiers, clusterers, associators, filters, loaders, savers, and stemmers) with associated methods.