Search Results for "feature selection"

DataPrep v0.2 is a Tkinter-based GUI application/tool designed to assist users in data preprocessing, multicollinearity removal, and feature selection for a wide range of applications in Cheminformatics, Bioinformatics, Data Analysis, Feature Selection, Molecular Modeling, Machine Learning, and Quantitative-structure-property relationship (QSPR) studies. It includes functionality to load, process, and save datasets with support for different preprocessing & multicollinearity removal strategies with customizable parameter setting options.

Java Machine Learning Library is a library of machine learning algorithms and related datasets. Machine learning techniques include: clustering, classification, feature selection, regression, data pre-processing, ensemble learning, voting, ...

A feature selection and classification algorithm. It is based on L1 Norm Support Vector Machine with t-test and Recursive Feature Elimination.

You may also like some newer work: http://createdatasol.com/ Project imDEV is an application of RExcel, which seamlessly integrates Excel and R for tasks focused on multivariate data visualization, exploration, and analysis. Interactive modules for dimensional reduction (imPCA), prediction (imPLS), feature selection, analysis of correlation (imCorrelations) and generation of networked structures (imGraph) provide an integrated environment for systems level analysis of multivariate data.

DimReduction project provide an open-source multiplatform (Java) graphical environment for bioinformatics problems that supports many feature selection algorithms, pattern recognition techniques, criterion functions and graphic visualization tools.

Java GUI to analyze one (or more, in batch mode) datasets with several feature selection methods at the same time and also performing an "ensemble" analysis. It can be easily extended to include any feature selection algorithm.

Tools to build molecular-docking activity prediction models by PLS regression with iterative training and pose-selection. Descriptors include (i) docking score(s), (ii) pharmacophore features, (iii) multi-feature descriptors learned by decision trees.