A training based approach to document classification classifies documents according to the similarity of unclassified documents to trained examples of that kind of document (or Document Type from Grooper's perspective).
There are two training based approaches in Grooper.
- This classification method trains text features (words and phrases) of examples documents. Document samples are trained as examples of a Document Type of a Content Model. Training occurs via user supervised machine learning using the TF-IDF algorithm. A Data Extractor set on the Text Feature Extractor property returns words, phrases or other results to provide possible identifiers used to classify a document. These identifiers (the words, phrases or other results from the Data Extractor used) are called "features." Document training uses TF-IDF to assign weightings to those features. During classification, Grooper looks at the weightings list for the various trained Document Types and compares them to the text features on the current document to be classified. The document is then assigned a percentage similarity score to each possible Document Type match. Whichever Document Type has the highest percentage similarity is used to classify the document.
- Note: This is the most common method. It is so common "training based approach" and "Lexical classification" are often used interchangibly.
- The Visual classification method uses image data instead of text data to determine the Document Type. Instead of using text extractors, an IP Profile will be set with an Extract Features command to get data pertaining to a document's image. Document samples are trained as examples of a Document Type.
- Note: While this is a much less commonly used method, it is still technically a training based approach to classification.