Supervised Classification Problems–Taxonomy of Dimensions and Notation for Problems Identification

Abstract

The paper proposes a taxonomy for categorizing the main features of the supervised learning classification problems and a notation for the identification of the supervised learning classification problem categories. The proposed taxonomy has been based on the review and analysis of the recent literature. It allowed the construction of the landscape of decision problem factors influencing the supervised learning processes. To enable a concise and coherent identification of supervised classification problems we have suggested a notation enabling description and identification of various supervised learning classification problem types and their critical features. The notation consists of 5 fields representing, in a sequence, a structure and properties of decision classes, structural model and properties of attributes, features of the data source, and the performance measure used for constructing and evaluating a classifier. The proposed notation is open and could be extended in the case of need new developments within the machine learning theory.

Keywords

• Machine learning
• supervised classification
• classification problems
• taxonomy of features
• notation for problem description