Package: mildsvm 0.4.0.9000

mildsvm: Multiple-Instance Learning with Support Vector Machines

Weakly supervised (WS), multiple instance (MI) data lives in numerous interesting applications such as drug discovery, object detection, and tumor prediction on whole slide images. The 'mildsvm' package provides an easy way to learn from this data by training Support Vector Machine (SVM)-based classifiers. It also contains helpful functions for building and printing multiple instance data frames. The core methods from 'mildsvm' come from the following references: Kent and Yu (2022) <arxiv:2206.14704>; Xiao, Liu, and Hao (2018) <doi:10.1109/TNNLS.2017.2766164>; Muandet et al. (2012) <https://proceedings.neurips.cc/paper/2012/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper.pdf>; Chu and Keerthi (2007) <doi:10.1162/neco.2007.19.3.792>; and Andrews et al. (2003) <https://papers.nips.cc/paper/2232-support-vector-machines-for-multiple-instance-learning.pdf>. Many functions use the 'Gurobi' optimization back-end to improve the optimization problem speed; the 'gurobi' R package and associated software can be downloaded from <https://www.gurobi.com> after obtaining a license.

Authors:Sean Kent [aut, cre], Yifei Liou [aut]

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mildsvm.pdf |mildsvm.html
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NEWS

# Install 'mildsvm' in R:
install.packages('mildsvm', repos = c('https://skent259.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/skent259/mildsvm/issues

Datasets:
  • ordmvnorm - Sample ordinal MIL data using mvnorm

On CRAN:

distributional-datamultiple-instance-learningordinalsvmweakly-supervised-learning

3.80 score 3 stars 42 scripts 147 downloads 23 exports 30 dependencies

Last updated 2 years agofrom:b58d05a274. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-winNOTENov 21 2024
R-4.5-linuxNOTENov 21 2024
R-4.4-winNOTENov 21 2024
R-4.4-macNOTENov 21 2024
R-4.3-winNOTENov 21 2024
R-4.3-macNOTENov 21 2024

Exports:as_mi_dfas_mild_dfbag_instance_samplingbuild_fmbuild_instance_featureclassify_bagscv_misvmgenerate_mild_dfkfm_exactkfm_nystromkmemimi_dfmildmild_dfmiormismmmisvmmisvm_orovaomisvmsmmsummarize_samplessvor_exc

Dependencies:classclicpp11dplyre1071fansigenericsgluekernlablifecyclemagrittrMASSmvtnormpillarpkgconfigplyrpROCproxypurrrR6Rcpprlangstringistringrtibbletidyrtidyselectutf8vctrswithr

Readme and manuals

Help Manual

Help pageTopics
Coerce to MI data frameas_mi_df
Coerce to MILD data frameas_mild_df
Sample 'mild_df' object by bags and instancesbag_instance_sampling
Build a feature map on new databuild_fm build_fm.kfm_exact build_fm.kfm_nystrom
Flatten 'mild_df' data to the instance levelbuild_instance_feature
Classify y from bagsclassify_bags
Fit MI-SVM model to the data using cross-validationcv_misvm cv_misvm.default cv_misvm.formula cv_misvm.mi_df
Printing multiple instance data framesformatting print.mild_df print.mi_df
Generate mild_df using multivariate t and normal distributions.generate_mild_df
Create an exact kernel feature mapkfm_exact
Fit a Nyström kernel feature map approximationkfm_nystrom kfm_nystrom.default kfm_nystrom.mild_df
Calculate the kernel mean embedding matrixkme kme.default kme.mild_df
Create an 'mi' objectmi
Build a multiple instance (MI) data framemi_df
Create a mild objectmild
Build a MILD data framemild_df
Fit MIOR model to the datamior mior.default mior.formula mior.mi_df
Fit MILD-SVM model to the datamismm mismm.default mismm.formula mismm.mild_df
Fit MI-SVM model to the datamisvm misvm.default misvm.formula misvm.mild_df misvm.mi_df
Fit MI-SVM model to ordinal outcome data using One-vs-Allmisvm_orova misvm_orova.default misvm_orova.formula misvm_orova.mi_df
Fit MI-SVM-OR model to ordinal outcome dataomisvm omisvm.default omisvm.formula omisvm.mi_df
Sample ordinal MIL data using mvnormordmvnorm
Predict method for 'cv_misvm' objectpredict.cv_misvm
Predict method for 'mior' objectpredict.mior
Predict method for 'mismm' objectpredict.mismm
Predict method for 'misvm' objectpredict.misvm
Predict method for 'misvm_orova' objectpredict.misvm_orova
Predict method for 'omisvm' objectpredict.omisvm
Predict method for 'smm' objectpredict.smm
Predict method for 'svor_exc' objectpredict.svor_exc
Fit SMM model to the datasmm smm.default smm.formula smm.mild_df
Summarize data across functionssummarize_samples summarize_samples.default summarize_samples.mild_df
Fit SVOR-EXC model to ordinal outcome datasvor_exc svor_exc.default svor_exc.formula svor_exc.mi_df