The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Changelog

utiml 0.1.7 (current)

Major changes

• Removing support to SMO and J48 base algorithms due to incompatibilites
• Removing method CTRL due to R dependencies issues

Bug fixes

• Throws an error message for ps, ppt and eps when all labelsets are pruned
• BugFix ps, ppt and eps when there is no labelsets to prune
• Weights passed incorrectly to xgboost by base learner
• Stop modifying the the .GlobalEnv, by changing .Random.seed
• Improvement in RAkEL letting the user define any value for m
• Improving documentation of some methods

utiml 0.1.6

• Fixes for CRAN incompatibilities

utiml 0.1.5

Minor changes

• cv method also returns the prediction

Bug fixes

• macro-AUC for constant score predictions
• validation fold
• set.seed suppress warnings

utiml 0.1.4

New Features

• MLKNN algorithm
• ranking-loss baseline
• label problem evaluation measures
• kfold bult-in method
• The foodtruck dataset
• ESL algorithm

Minor changes

• confusion matrix in matrix format

Bug fixes

• Stratification sampling to support instances without labels
• Fixed threshold with multiple values
• Update documentation

utiml 0.1.3

Major changes

• Change multilabel_evaluation to also return the label measures

Bug fixes

• Bugfix in brplus because the newfeatures were using different levels
• Fix baseline using hamming-loss to prevent empty label prediction
• Fix empty prediction when all labels have the same probability

Minor changes

• Fix type mistakes in documentation

utiml 0.1.2

Major changes

• change base.method parameter name for base.algorithm

Bug fixes

• Bugfix in homer to deal with labels without intances and to predict instances based on the meta-label scores
• Refactory of merge_mlconfmat
• Ensure reproducibility in all cases

utiml 0.1.1

New multi-label transformation methods including pairwise and multiclass approaches. Some fixes from previous version.

Major changes

• lcard threshold calibration
• Use categorical attributes in multilabel datasets and methods
• LIFT multi-label classification method
• RPC multi-label classification method
• CRL multi-label classification method
• LP multi-label classification method
• RAkEL multi-label classification method
• BASELINE multi-label classification method
• PPT multi-label classification method
• PS multi-label classification method
• EPS multi-label classification method
• HOMER multi-label classification method

Minor changes

• Add Empty Model as base method to fix training labels with few examples
• multilabel_confusion_matrix accepts a data.frame or matrix with the predicitons
• Change EBR and ECC to use threshold calibration
• Include empty.prediction configuration to enable/disable empty predictions

Bug fixes

• Majority Ensemble Predictions Votes
• Majority Ensemble Predictions Probability
• Base method not found message error
• Base method support any attribute names
• Normalize data ignore attributes with a single value
• MBR support labels without positive examples
• Fix average precision and coverage measures to support instances without labels

utiml 0.1.0

First release of utiml:

• Classification methods: Binary Relevance (BR); BR+; Classifier Chains; ConTRolled Label correlation exploitation (CTRL); Dependent Binary Relevance (DBR); Ensemble of Binary Relevance (EBR); Ensemble of Classifier Chains (ECC); Meta-Binary Relevance (MBR or 2BR); Nested Stacking (NS); Pruned and Confident Stacking Approach (Prudent); and, Recursive Dependent Binary Relevance (RDBR)
• Evaluation methods: Create a multi-label confusion matrix and multi-label measures
• Pre-process utilities: fill sparse data; normalize data; remove attributes; remove labels; remove skewness labels; remove unique attributes; remove unlabeled instances; and, replace nominal attributes
• Sampling methods: Create subsets of multi-label dataset; create holdout and k-fold partitions; and, stratification methods
• Threshold methods: Fixed threshold; MCUT; PCUT; RCUT; SCUT; and, subset correction
• Synthetic dataset: toyml

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.