Introduction | Multiple signature selection with SES algorithm | Package implementation | Using SES | Experimental validation | Discussion and conclusions | Acknowledgments

Introduction | Overview of the feature selection algorithms and types of data MXM can handle | Overview of the algorithms in R | Demonstration of the algorithms using real data

Introduction: The MXM R package for Feature Selection | Overview of the MMPC algorithm | Selecting the appropriate Conditional Independence Test | The MMPC() function: Required and optional arguments | Applying the MMPC algorithm on the UCI wine dataset | Exploratory Data Analysis: Inspecting the wine dataset | Preparing the MMPC() input objects: 'target', 'dataset' | First run of MMPC() | MMPC() output: The mmpcobject and the feature signature | MMPC() subsequent run: Efficiency with the ini and hashObject arguments | Grid search for hyperparameter tuning: the mmpc.path() function | Optimized performance tips and tricks | Permutation option | Choosing the appropriate max_k for the conditioning set | Parallel computing: Choose number of cores option | References

Introduction | Loading Data | SES for Continuous | Running SES for 1st time | Selecting Appropriate Conditional Independence Test | Creating Data & Target Matrices | Function's Arguments | Output | Running SES 2nd time | Creating the model | SES for Categorical | Running SES for Categorical | Setting the Arguments | Permutation Based SES | Cross - Validation | Tips | Choosing k_max | Choosing number of cores | Conclusion | Session Info | References

Introduction | Feature selection related functions | Networks | Acknowledgments