Supervised classification

Supervised classification is a process to find a model, or function, by analyzing the attributes of a data set of which the class memberships are known. This function is then used to predict the class memberships for target population.

Essentially, supervised classification is a two-step approach.

The first step is to build a learning model to describe predetermined classes for a data set. The construction of the learning model is based on the analysis of data items or concepts for which class memberships are predetermined or known: data items known collectively as training samples.

In the second step, the learned model is applied to new (target) data items to predict their class membership. The supervisory component of this procedure is in the training phase, which provides the classifier with a way to assess a dependency measure between attributes and classes. With the unsupervised method, no such learning is involved.

To perform supervised classification, Object Analyst applies the Support Vector Machine (SVM) algorithm.

For more information, see:

  1. Editing training sites
  2. Detecting outliers
  3. Interquartile range
  4. Mean plus or minus three standard deviations
  5. Mean plus or minus two standard deviations
  6. Support Vector Machine classifier
  7. Random Trees classifier
Parent topic: Classification
Next topic: Unsupervised classification

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