About: The SSA Toolbox is an efficient, platform-independent, standalone implementation of the Stationary Subspace Analysis algorithm with a friendly graphical user interface and a bridge to Matlab. Stationary Subspace Analysis (SSA) is a general purpose algorithm for the explorative analysis of non-stationary data, i.e. data whose statistical properties change over time. SSA helps to detect, investigate and visualize temporal changes in complex high-dimensional data sets.

• Various bugfixes.

About: Mulan is an open-source Java library for learning from multi-label datasets. Multi-label datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multi-label dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions.

Learners

• New algorithms added in the meta package.
• EnsembleOfClassifierChains: The final confidences can now be computed not only by averaging votes, but also by averaging confidences. The option of sampling with replacement was added.
• MMP: updated with loss functions. Added possibility to specify number of training epochs for MMPLearner.
• BinaryRelevance: Added method to get the model built for a label.
• Update to the lazy package: Euclidean is still the default distance function, the option to use a different distance function is given.

Measures

• Introduced loss functions package.
• Refurbished the measures package so that the measure hierarchy has cleaner semantics and takes loss functions into consideration.
• Strict/nostrict evaluation (handles divisions by zero differently).
• Uniform calculation of f-measure for all related measures.

Bug fixes

• Bug fix in the dimensionality reduction package.
• Bug fix in CalibratedLabelRanking class.
• Updated design and bug fixes in thresholding strategies.
• Fixed defect in MMPUniformUpdateRule.
• Bug fix in the getPriors method.

API changes

• Upgrade to Weka 3.7.3.

Experiments

• Experiment from ICTAI 2010 paper added.

Examples

• Simplified source examples for consistency with the online documentation.
• Added an example that shows storing/loading a multi-label model.

Unit Tests

• HOMER and HMC tests added.
• MetaLabeler and ThresholdPrediction test updated.