An Investigation to Improve Classifier Accuracy for Myo Collected Data

Abstract

A nave Bayes classifier trained with 1,360 samples from 17 volunteers performs at an accuracy of 72.5 (based on 10-fold cross validation). This accuracy is based on using the entire data set. One approach to increasing the accuracy is by analyzing the data and removing irregular samples from the training set. As the quality of the training data increases, the accuracy of the classifier will increase. This report describes analysis of 2 features of the training data, observed unusual patterns, and how fine tuning the training set increased the accuracy by 7.2 .

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2017
Accession Number
AD1026237

Entities

People

  • Andre V. Harrison
  • Michael H. Lee
  • Robert P . Winkler

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Information Processing
  • Information Science
  • Machine Learning
  • Military Research
  • Nato
  • Precision
  • Predictive Modeling
  • Statistical Analysis
  • Statistics
  • Training
  • Validation
  • Volunteers

Fields of Study

  • Computer science

Readers

  • Instructional Design and Training Evaluation.
  • Mathematics or Statistics
  • Neural Network Machine Learning.