The dangers of creating false classifications due to noise in electronic nose and similar multivariate analyses

By K. Goodner, J. Dreher, R. Rouseff. 2001

69 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/9b524b9dae9446c017b323abdec611e534c5a023

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Alphus D. Wilson

In Biomedical Engineering

1. Elizabeth A. Baldwin
2. Sharon Dea
3. Anne Plotto
4. Jinhe Bai
5. Alphus D. Wilson
6. Giovanni Neri

In Control and Systems Engineering

1. Alphus D. Wilson

Citations in the corpus

• Diverse Applications of Electronic-Nose Technologies in Agriculture and Forestry (2013, methodology): “[25] found noise-based false classifications could be minimized by increasing samples sizes, using a minimum number of variables (features) when developing classification models to avoid over-fitting data, making sure the ratio of data points to variables is at least six to prevent over-fitting clas”
• Electronic Noses and Tongues: Applications for the Food and Pharmaceutical Industries (2011, background): “One must be careful, however, of false classification and over-fitting data, creating artificial differentiation especially in cases where the samples to variables ratio falls below six, when misclassifications can occur [3].”