RapidMiner InfoSel Extension

This is a source code for RapidMiner Extansion called Information Selection, previouselly called ISPR.

This extension implements instance selection algorithms as well as some clustering methods and other extra features.

Implemented Instance selection algorithms:

• CNN - Condensed Nearest Neighbor Rule
• ENN - Edited Nearest Neighbor Rule
• RENN - Repeated Edited Nearest Neighbor Rule
• All k-NN
• RNG - Relative Neighbor Graph
• GE - Gabriel Editing
• ELH - Encoding Length Heuristic
• RMHC – Random Mutation Hill Climbing
• IB2 - Instance Based Learning v2
• IB3 - Instance Based Learning v2
• Drop1 - Decremental Reduction Optimization Procedure v1
• Drop2 - Decremental Reduction Optimization Procedure v1
• Drop3 - Decremental Reduction Optimization Procedure v1
• Drop4 - Decremental Reduction Optimization Procedure v1
• Drop5 - Decremental Reduction Optimization Procedure v1
• ICF - Iterative Case Filtering
• MC - Monte Carlo
• Random Selection Note: The most of these algorithms supports regression problems with additive as well as multiplicative noise

The extension also wraps other instance selection libraries:

Instance Selection for Weka by Álvar Arnaiz-González:

• Weka Drop1-5
• Weka ICF
• Weka BSE
• Weka CNN
• Weka ENN
• Weka HMNE
• Weka HMNEI
• Weka MC
• Weka MSS
• Weka RNN

Instance Selection methods from Keel project:

• Keel CCIS

Ensembles of Instance Selection methods:

• Ensemble Instance Selection by Bagging
• Ensemble Instance Selection by Voting
• Ensemble Instance Selection by Attribute Subsets
• Ensemble Instance Selection by Noise
• Ensemble Instance Selection by AdaBoost

Generalized Instance Selection (these methods allow any classifier to be used within instance selection)

• Generalized ENN
• Generalized CNN

Competitive based Neural Networks:

• LVQ1
• LVQ2
• LVQ2.1
• LVQ3
• OLVQ
• Weighted LVQ
• SNG – Supervised Neural Gas
• Winer Takes Most LVQ
• Generalized LVQ

Clustering algorithms:

• Fuzzy c-means
• Vector Quantization
• Conditional Fuzzy c-Means

Feature set reduction algorithms

• MDS Multidimensional Scaling (use external library: Creative Commons License - which is not fully compatible with AGPL)
• Feature Selection based on Infosel++ Package (requires external c++ library)

Performance metrics for:

• Instance selection
• Clustering

Preprocessing methods:

• VDM based feature transformation from categorical into numerical features

Noise estimation methods:

• Gamma-Test
• Delta-Test
• Local Gamma-Test
• Local Delta-Test
• ENN based instance weighting

Some other usefull operators

Requirenments

Prerequisite

• Requires Gradle 2.3+ (get it here or use the Gradle wrapper shipped with this template)
• RapidMiner 7 or later

Getting started

1. Clone the extension
2. Build and install your extension by executing the installExtension Gradle task
3. Start RapidMiner Studio and check whether your extension has been loaded