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