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What's new in Weka 3.8.0 and 3.9.0

What's new in Weka 3.8.0 and 3.9.0

Apr 14, 2016

In core weka

  • JAMA-based linear algebra routines replaced with MTJ. Faster operation with the option to use native libraries for even more speed

  • General efficiency improvements in core, filters and some classifiers

  • GaussianProcesses now handles instance weights

  • New Knowledge Flow implementation. Engine completely rewritten from scratch with a simplified API

  • New Workbench GUI

  • GUI package manager now has a search facility

  • FixedDictionaryStringToWordVector filter allows the use of an external dictionary for vectorization. DictionarySaver converter can be used to create a dictionary file

In packages

  • Packages that were using JAMA are now using MTJ

  • New netlibNativeOSX, netlibNativeWindows and netlibNativeLinux packages providing native reference implementations (and system-optimized implementation in the case of OSX) of BLAS, LAPACK and ARPACK linear algebra

  • New elasticNet package, courtesy of Nikhil Kinshore

  • New niftiLoader package for loading a directory with MIR data in NIfTI format into Weka

  • New percentageErrorMetrics package - provides plugin evaluation metrics for root mean square percentage error and mean absolute percentage error

  • New iterativeAbsoluteErrorRegression package - provides a meta learner that fits a regression model to minimize absolute error

  • New largeScaleKernelLearning package - contains filters for large-scale kernel-based learning

  • discriminantAnalysis package now contains an implementation for LDA and QDA

  • New Knowledge Flow component implementations in various packages

  • newKnowledgeFlowStepExamples package - contains code examples for new Knowledge Flow API discussion in the Weka Manual

  • RPlugin updated to latest version of MLR

  • scatterPlot3D and associationRulesVisualizer packages updated with latest Java 3D libraries

  • Support for pluggable activation functions in the multiLayerPerceptrons package