NEWS.md

In fcharte/mldr: Exploratory Data Analysis and Manipulation of Multi-Label Data Sets

Changelog

mldr 0.4

• Reimplementation of performance evaluation metrics
• Customizable treatment of undefined values in evaluation metrics
• Support for single-quoted/double-quoted attributes in ARFF files
• Support for stringsAsFactors
• Support for negative -C parameter in MEKA header
• API changes: The return value of mldr_evaluate has the same structure but different member names.

mldr 0.3.22

• Improve how we load available MLD's in mldr()
• Update CITATION file

mldr 0.3.18

• Fix bug #21 when reading sparse datasets introducing zeroes instead of NAs
• Fix #23 by ignoring case in @attribute tags
• Fix bug #24 in filtering function
• Improves parsing of attributes to correctly manage escaped apostrophes
• Fix calculation of SCUMBLE CV to prevent NaN values
• Optimizes some calculations (mean and CV of SCUMBLE are now 4x faster)
• Export read.arff function to allow reading multilabel data without calculating measures
• Add ability to load datasets from the mldr.datasets package in mldr() function

mldr 0.2.82

• Fix bug in demo code for rebuilding an mldr object
• Add global scumble.cv measure and SCUMBLE.CV measure per label
• Add new concurrence module to ease analysis of concurrence among imbalanced labels
• Display an analysis of concurrence between labels within the GUI
• Add remedial preprocessing algorithm
• Fix bug #18 when reading ARFF with capitalized '@relation' parameters
• A Citation file (accesible from citation("mldr")) has been added

mldr 0.2.51

• Fix bug #14 when reading certain sparse datasets (e.g. Yahoo)
• Support for multiple plot types in one call
• Add num.inputs measure for input attributes
• Add color parameters for plotting functions

mldr 0.2.33

• New redesigned GUI to ease usability
• Add ability to read MEKA datasets from GUI

mldr 0.2.25

New features

• New mldr_evaluate() function to assess multilabel classifier performance. Taking as input an mldr object and a matrix with predictions this function returns a list of metrics, including Accuracy, AUC, AveragePrecision, Coverage, FMeasure, HammingLoss, MacroAUC, MacroFMeasure, MacroPrecision, MacroRecall, MicroAUC, MicroFMeasure, MicroPrecision, MicroRecall, OneError, Precision, RankingLoss, Recall, and SubsetAccuracy
• Added more parameters to mldr function so labels can be identified via specific names, indices or their count.
• Added vignette

Fixes

• Fixed call to chordDiagram for newer versions of the circlize package.
• Fixed imports to avoid NOTEs on devel builds.
• Fixed parameters in calls to pROC functions.

mldr 0.1.70

First release of mldr. This version includes:

• Ability to read multi-label data sets from ARFF and XML files in Mulan or MEKA format.
• Ability to write to ARFF and XML files in both Mulan and MEKA formats.
• Different ways to display data and relevant measures from data sets.
• Several plots for multi-label data visualisation.
• Functions to operate with mldr objects: filtering, joining and structure comparison.
• BR and LP transformations of multi-label datasets.
• Ability to create new mldr objects out of data.frames.
• Sample datasets: birds, emotions and genbase.

fcharte/mldr documentation built on Dec. 16, 2019, 12:56 p.m.