Open AccessSupervised Classification Problems–Taxonomy of Dimensions and Notation for Problems Identification
Author(s) -
Ireneusz Czarnowski,
Piotr Jedrzejowicz
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3125622
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.