NEWS
utiml 0.1.7 (2021-05-31)
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 (2020-02-07)
- Fixes for CRAN incompatibilities
utiml 0.1.5 (2019-03-16)
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 (2018-04-19)
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 (2017-07-31)
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 (2017-04-06)
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 (2016-11-19)
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 (2016-04-08)
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