Configuring the Engine Options

Use the Engine Options tab to indicate the Method and Metric values required for sample classification.

Select a Method on the Engine Options tab to configure the method of classification.

• Nearest Neighbor—Most direct approach to classification. In nearest neighbor classification, the distance of an input feature vector of unknown class to another class is defined as the distance to the closest samples that are used to represent that class.
• K-Nearest Neighbor—More tolerant of noise compared with nearest neighbor classification. In K-nearest neighbor classification, an input feature vector is classified into a class based on a voting mechanism. The NI Classifier finds K nearest samples from all the classes. The input feature vector of unknown class is assigned to the class with majority of the votes in the K nearest samples.
• Minimum Mean Distance—Most effective in applications that have little or no feature pattern variability or other corruptive influences. In minimum mean distance classification, an input feature vector of unknown class is classified based on its distance to each class center.

Select a Metric on the Engine Options tab to configure the metric used in the classification algorithm.

• Maximum—Most sensitive to small variations between samples. Use Maximum when you need to classify samples with very small differences into different classes.
• Sum—Metric used in most classification applications. Sum is also known as the Manhattan metric or Taxicab metric. This is the default Metric value.
• Euclidean—Least sensitive to small variations between samples. Use Euclidean when you need to classify samples with small differences into the same class.

Tip  If the samples in the ROI are not classified as you expect them to be, experiment with settings on the Preprocessing and Particle Classifier Options tabs to improve the classification.