Techniques for Dimensionality Reduction

This workflow performs classification on data sets that were reduced using the following dimensionality reduction techniques:
- Linear Discriminant Analysis (LDA)
- Auto-encoder
- t-SNE
- Missing values ratio
- Low variance filter
- High correlation filter
- Ensemble tree
- PCA
- Backward feature elimination
- Forward feature selection
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The performances of the classification models are compared to the performance that is achieved when all columns are retained in terms of overall accuracy and AuC statistics. These evaluation metrics are produced by the best performing classification model out of this bag of models:
- Multilayer Feedforward Neural Networks
- Naive Bayes
- Decision Tree

External resources

• KDD Cup 2009 Data
• Seven Techniques for Data Dimensionality Reduction
• Random Forest for Data Dimensionality Reduction
• Paper Dissected: "Visualizing Data Using t-SNE" Explained
• Linear discriminant analysis
• Neural networks [6.1] : Autoencoder - definition
• Principal component analysis