Exploring Dimensionality Reductions with Forward and Backward Projections

Clustrophile: A Tool for Visual Clustering Analysis

While clustering is one of the most popular methods for data mining, analysts lack adequate tools for quick, iterative clustering analysis, which is essential for hypothesis generation and data reasoning. We introduce Clustrophile, an interactive tool for iteratively computing discrete and continuous data clusters, rapidly exploring different choices of clustering parameters, and reasoning abou...

A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration

Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making...

A Random Forest Classifier based on Genetic Algorithm for Cardiovascular Diseases Diagnosis (RESEARCH NOTE)

Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimen...

Forward and Backward Uncertainty Quantification in Optimization

This contribution gathers some of the ingredients presented during the Iranian Operational Research community gathering in Babolsar in 2019.It is a collection of several previous publications on how to set up an uncertainty quantification (UQ) cascade with ingredients of growing computational complexity for both forward and reverse uncertainty propagation.

Seven Techniques for Data Dimensionality Reduction Missing Values, Low Variance Filter, High Correlation Filter, PCA, Random Forests, Backward Feature Elimination, and Forward Feature Construction