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Changelog

utiml 0.1.5 (development)

Minor changes

  • cv method also returns the prediction

Bug fixes

  • macro-AUC for constant score predictions

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