All Relevant Feature Selection

Implementations of various feature selection methods

This is an App developed in Python to implement the algorithm for minimum redundancy maximum ralevance. The formulation was based on a research paper from Chris Ding and Hanchuan Peng (Minimum Redundancy Feature Selection from Microarray Gene Expression Data).

Implementation of various feature selection methods using TensorFlow library.

Maximum Relevance Minimum Redundancy for big datasets

Conformal Inference tools using python

Feature selection in Apache Spark using Minimum Redundancy and Maximum Relevance

Diabetes Prediction using Three Machine Learning Algorithms - Logistic Regression, Random Forest & SVM

A project that focuses on implementing a hybrid approach that modifies the identification of biomarker genes for better categorization of cancer. The methodology is a fusion of MRMR filter method for feature selection, steady state genetic algorithm and a MLP classifier.