Hybrid Feature Selection with Genetic Algorithms and other Methods

Abstract

Feature Selection (FS) processes are becoming more important for image-base cell profiling as the field begins to rely more heavily on computational means of analysis. A single image analysis can be scanned for more than a thousand different features each with varying metrics. Current researchers have realized the holistic approach to measure every possible feature lead to the issue of determining the features that provide an adequate experimental analysis. Past FS methods were to preselect features from a smaller feature set, however, increases in computational speed and advances in machine learning methods have allowed quick expansive analysis to become more prevalent.

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Document Details

Document Type
Technical Report
Publication Date
Oct 16, 2020
Accession Number
AD1123160

Entities

People

  • Daniel W. Cowan
  • Heather A. Pangburn
  • Patrick M. Mclendon
  • Timothy Ho

Organizations

  • Rensselaer Polytechnic Institute

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Biomedical Engineering
  • Biotechnology
  • Contracts
  • Data Analysis
  • Data Mining
  • Data Science
  • Engineering
  • Extraction
  • Feature Extraction
  • Feature Selection
  • Genetic Algorithms
  • Governments
  • Information Science
  • Learning
  • Machine Learning
  • Military Research
  • Motor Skills
  • Neural Networks
  • Preprocessing
  • Test And Evaluation

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Biotechnology